Systems, methods, and apparatus for in vitro single-cell identification and recovery

ABSTRACT

Described herein are systems, methods, and apparatus for automatically identifying and recovering individual cells of interest from a sample of biological matter, e.g., a biological fluid. Also described are methods of enriching a cell type of interest. These systems, methods, and apparatus allow for coordinated performance of two or more of the following, e.g., all with the same device, thereby enabling high throughput: cell enrichment, cell identification, and individual cell recovery for further analysis (e.g., sequencing) of individual recovered cells.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Divisional Application of U.S. application Ser.No. 14/997,439, filed on Jan. 15, 2016, which claims the benefit of U.S.Provisional Application No. 62/104,036, filed Jan. 15, 2015, thecontents of which are hereby incorporated by reference herein in theirentireties.

GOVERNMENT SUPPORT

This invention was made with Government support under Grant Nos. R21AI106025 and 1-R56-AI104274-01 awarded by the National Institutes ofHealth and under Contract No. W911NF-13-D-0001 awarded by the U.S. ArmyResearch Office. The Government has certain rights in the invention.

FIELD OF THE INVENTION

The invention relates generally to systems and methods for in vitrosingle-cell identification and recovery (e.g., high throughput).

BACKGROUND

The last several decades have seen tremendous progress in theunderstanding of biological processes. Despite these advances, researchin many important fields, such as immunology and cancer biology, hasmade it increasingly clear that bulk measurements can maskcharacteristics of individual cells or subsets of cells. Such individualcells and small subsets of cells may contribute significantly tobiological processes, yet may not be identical to the population averagemeasured by existing techniques. In addition, interactions betweenindividual players may not be resolved if only an average behavior isstudied. As a result, traditional methods may draw a misleading pictureof dynamic responses of cells to the given perturbations of theirbiological environments, necessitating development of technologies forsingle-cell analysis. Moreover, inefficiencies in sample handling anddata collection inherent in current flow-based profiling methods (e.g.,flow cytometry) limit comprehensive phenotyping of the scarce cellsrecovered from tissue samples.

Conventional slide-based cytometry can efficiently provide capture ofall cells in the sample in a first step, preventing cell loss duringcell staining and data acquisition. However, current methods ofacquiring images of the captured cells, such as laser scanning slidecytometry and multi-parameter confocal microscopy, have (1) lagged onthe number of channels available on state-of-the art flow cytometers and(2) are costly, which restrict their availability primarily to corefacilities. Moreover, while conventional methods using iterativestaining have expanded the number of markers that can be detected, theyare both labor and time intensive.

Further, typical slide-based cytometry methods require the cells to befixed to the slide, thereby preventing further analysis of theseprecious cell samples using functional assays, which are critical forunderstanding the role of these cells in tissue-restricted immuneresponses.

There is a need for the development of efficient methodologies forinterrogating cells (e.g., lymphocytes, leukocytes, tumor cells, stromalcells, neuronal cells, cell lines (e.g., CHO cells, NS0 cells), stemcells, embryos, and the like) present in scarce cell samples to advanceunderstanding of clinical responses to the growing number ofexperimental interventions targeting tissue in the fields of cancerimmunotherapy, autoreactive bowel disorders, allergy, infectiousdisease, multiple sclerosis, neuroimmunological disease, and HIV.Development of such methods must have the ability to image a large areafor increased throughput, have a large spectral depth (e.g., 10-30 colorchannels for 10-30 markers), automatically scan large area for scarcecells, pick the scarce cells, and maintain cell viability for furtherfunctional characterization.

SUMMARY

Described herein are systems and methods for automatically identifyingand recovering individual cells of interest from a sample of biologicalmatter, e.g., a biological fluid, tumor biopsies, punch biopsies, skinsamples, cytobrushes, lavages, fine needle aspirates, cerebrospinalfluid, synovial fluid, blood, sputum, urine, etc. Also described aremethods of enriching a cell type of interest (e.g., lymphocytes,leukocytes tumor cells, stromal cells, neuronal cells, cell lines (likeCHO cells, NS0 cells), stem cells, embryos, and the like). These systemsand methods offer advantages over pre-existing systems in that theyallow automated (or semi-automated) identification and recovery ofindividual cells at a high throughput. The systems and methods alsoallow for manual verification of automatically-identified candidatecells, which may be advantageous for satisfaction of certain regulatoryrequirements, while still allowing for high throughput.

The systems and methods described herein allow for coordinatedperformance of two or more of the following, e.g., all with the samedevice: cell enrichment, cell identification, individual cell recovery,and analysis (e.g., sequencing) of individual, recovered cells.Moreover, the systems and methods have the ability to (i) image largeareas of tissue samples and biopsies for increased throughput, (ii) havea large spectral depth (e.g., 10-30 color channels for 10-30 markers),(iii) automatically scan large areas for scarce cells, (iv) pick thescarce cells, (v) maintain cell viability for further functionalcharacterization (cells can be kept alive during processing), and/or(vi) provide dynamic and secretory measurements of individual cells. Incontrast to conventional systems, which take days for identifying celltypes and relevant information, these systems and methods can provideresults and information in under 20 minutes.

As one example, circulating tumor cells (CTCs) are rare tumor cellsfound in the blood of cancer patients (˜1 ppm mononuclear cells) and arebelieved to be responsible for disseminating cancer (metastasis). Thenumbers of CTCs found in blood can serve as a prognostic indicator incertain tumor types. CTCs offer many opportunities beyond enumeration.Indeed, molecular analysis of CTCs may reveal information about solidtumor lesions and allow monitoring of the progression of disease fromblood samples. Along with the analysis of circulating tumor DNA, such“liquid biopsies” offer a real-time, minimally-invasive window intometastasis that would not be feasible using repeated surgical biopsies.

Sequencing-based analyses of CTCs allow the tracing of lineage-specificevolution of tumors, assessment of clonal heterogeneity, andidentification of mechanisms of resistance to therapies. However, theinherently small amount of material available from each cell (e.g., only1 copy of each parental allele) necessitates the use of amplificationmethods prior to sequencing that introduce biases and errors whichconfound the confident calling of mutations and copy-number alterations.Census-based methods enable accurate and powered calling of somaticalterations from CTCs, but require isolating, amplifying, and sequencingmultiple independent CTCs, and thus are limited when an insufficientnumber of cells is available. As such, increasing the number of singleCTCs recovered from a given volume of blood (or processing largervolumes in a given time) is paramount to performing more confident anddetailed analyses, along with expanding the (sometimes incompatible)types of analysis performed on each sample.

In one aspect, the invention is directed to a multiscale deposition-wellplate (e.g. for use with a system for automated identification and/orrecovery of individual cells of interest as described herein) comprisingone or more sample wells (e.g., from three to twenty, or from three totwelve) and zero or more recovery wells (e.g., 24, 48, 96, at least 24,at least 48, at least 96, etc.).

In certain embodiments, the multiscale deposition-well plate comprises aplurality (e.g., an array) of macro-scale wells (e.g., each macro-scalewell with any one or more of length, width, and/or depth of at least 1mm, at least 3 mm, at least 5 mm, or at least 8 mm, and/or with any oneor more of length, width, and/or depth no greater than about 100 mm, nogreater than about 50 mm, or no greater than about 25 mm), wherein eachof the macro-scale wells comprise a plurality of micro-scale and/ornano-scale wells (e.g., each micro-scale well with any one or more oflength, width, and/or depth of at least 1 μm, at least 5 μm, or at least10 μm, and/or with any one or more of length, width, and/or depth nogreater than about 1000 μm, no greater than about 500 μm, no greaterthan about 250 μm, or no greater than about 100 μm) (e.g., eachnano-scale well with any one or more of length, width, and/or depth ofat least 1 nm, at least 5 nm, or at least 10 nm, and/or with any one ormore of length, width, and/or depth no greater than about 1000 nm, nogreater than about 500 nm, no greater than about 250 nm, or no greaterthan about 100 nm) (e.g., wherein each micro-scale well contains nogreater than about 1000 pL of sample volume, no greater than about 500pL of sample volume, no greater than about 100 pL of sample volume, orno greater than about 50 pL of sample volume, or no greater than about10 pL).

In certain embodiments, each of the wells has a sample-contactingsurface compatible with cells (e.g., live cells) (e.g., e.g.,lymphocytes, leukocytes, tumor cells, stromal cells, neuronal cells,cell lines (e.g., CHO cells, NS0 cells), stem cells, embryos, and thelike), the sample-contacting surface comprising one or more membersselected from the group consisting of glass, silicon, polymer (e.g.,polycarbonate, polystyrene, epoxy, ABS plastics, polypropylene, orfluoropolymer), elastomer (e.g., polydimethylsiloxane), a thermoplastic,and a medical grade plastic.

In another aspect, the invention is directed to a deposition well-platecomprising one or more sample wells and one or more recovery wells,wherein the sample well(s) and recovery well(s) are positioned inproximity to each other (e.g. to require only minimal travel of acell-recovery implement (e.g., needle) from a sample well to a recoverywell, e.g., during the process of physical retrieving of a cell from asample well to a recovery well) (e.g, wherein each sample well is within100 mm, 50 mm, 25 mm, 10 mm, or 5 mm of the nearest recovery well).

In certain embodiments, at least one of the sample well(s) and/or atleast one of the recovery well(s) is a multiscale deposition well. Incertain embodiments, the deposition well-plate comprises one or morewash stations. In certain embodiments, at least one of the sample wellscomprises tapered walls (e.g., tapered at least 1 degree from vertical,at least 2 degrees from vertical, at least 3 degrees from vertical, atleast 5 degrees from vertical, at least 7 degrees from vertical, atleast 10 degrees from vertical, or at least 15 degrees from vertical,e.g., to allow cell picking close to edges).

In another aspect, the invention is directed to a system for performingmultispectral cytometry (e.g., multicolor slide cytometry), the systemcomprising a processor of a computing device coupled to computer memoryand/or operable to execute a set of pre-defined instructions (e.g., runcomputer software) to perform one or more of (i) to (iv) as follows(e.g., performing any 1, 2, 3, or all 4 of (i) to (iv)) (e.g., whereinall of the one or more step (i) to (iv) are performed in no greater than30 minutes, no greater than 10 minutes, or no greater than 5 minutes fora given imaging task): (i) automated image calibration (e.g.,calibration of image of cells in a micro-/nano-/pico-well grid) based onone or more of (a) to (d): (a) a raw image, (b) a dark frame, (c) a flatfield frame, and (d) an illumination field frame; (ii)micro-/nano-/pico-well grid identification; (iii) cell identificationand/or data extraction using one or both of (e) and (f): (e) asegmentation thresholding technique in which the threshold is based(e.g., solely) on a distribution of detected background pixels; and (f)a signal-to-noise maximization technique in which an aperture (e.g., offrom 5 to 8 pixels) is set within a defined cell area to minimizedilution of signal from a decrease of signal near the cell periphery,and (iv) spectral spillover compensation (e.g. using an in silicoF-minus one technique, e.g., as described herein below).

In another aspect, the invention is directed to a system for performingspectral spillover compensation in multicolor slide cytometry, thesystem comprising at least one memory and a processor of a computingdevice communicatively coupled to the at least one memory, (e.g., thesystem also comprising an imaging device) wherein the processor isoperable to perform one or more of steps (i) to (xi) as follows (e.g.,performing any 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or 11 of steps (i) to(xi)): (i) identify location of one or more beads; (ii) extract a signalintensity of each pixel in each of a plurality of spectral channels(e.g., from 10 to 30 spectral channels) for each bead; (iii) create oneor more 3D probability matrices relating intensity of signal in thespectral channel assigned to a fluorophore to the signal in each of theother channels; (iv) identify a location of cells in one or more images;(v) extract a signal in each of the plurality of spectral channels foreach cell; (vi) extract a background signal (e.g., from one or moreareas similar in size to an area from which a cell signal is extracted);(vii) determine an amount of each flurophore on each cell using one ormore average spillover values extracted from the one or more probabilitymatrices (e.g., and using standard linear compensation); (viii) createn-replicas of the compensated fluorophore content of each cell (e.g., ineach replica, one fluorophore content is zeroed by replacing the valuewith a sample taken from the background signal distribution); (ix)sample at least one of the one or more 3D probability matrices tocalculate an expected distribution of raw fluorescent signal in eachchannel based on concentration of each fluorophore; (x) compensatereconstructed pseudo-raw fluorescent values to create a distribution ofcalculated signal on cells identified as having no actual fluorophorespresent (e.g., population-level in silico FMOs); and (xi) resample aplurality of times (e.g., from 5 k to 100 k, or from 10 k to 100 k, orfrom 10 k to 1M times) for each cell to generate an expected negativecell distribution for each individual cell (e.g., single cell in silicoFMOs).

In certain embodiments, the processor is operable to perform step (iii)(create the one or more 3D probability matrices) by performing (a) to(e), as follows: (a) determine an average amount of light emitted inchannel B by fluorophore A (e.g., through linear regression); (b)normalize B signal to 0 (e.g., by subtracting a product fluorophore Aconcentration and slope of the linear regression in step (a)); (c) bindata into overlapping bins based on fluorophore A concentration; (d)create a 2D probability distribution of B signal for each bin (e.g.,normalized to 1); and (e) combine the 2D distributions into a 3Dspectral probability matrix.

In another aspect, the invention is directed to a system for hardwaretriggering of light sources for image acquisition (e.g., in multicolorcytometry, e.g., multicolor slide cytometry), the system comprising: acomputer with processor operable (e.g., programmed) to: transmit spatialpositions to a memory of a Stage, and filter positions to a memory of aFilter Wheel; transmit one or other parameters of acquisition to amicrocontroller (e.g., wherein the one or more parameter comprises anumber of positions and/or spectral channels to be acquired, one or moreLight Source(s), exposure times, and/or Filter Wheel movements set foreach channel), wherein the microcontroller is operable to start a cycleof image acquisition by signaling the Stage to move to a next positionstored in its memory, wherein the Stage moves to the stored position andsignals the microcontroller upon completion of the move, wherein themicrocontroller signals the Filter Wheel to move to the next positionstored in its memory, after which the Filter Wheel moves to the storedposition and signals the microcontroller upon completion of the move,upon which the microcontroller signals the Light Source(s) to turnit/them on, then signals a Detector to begin integration of light, andupon completion of exposure time, the microcontroller is operable tosignal the Light Source(s) to turn them off, after which themicrocontroller signals the Detector to stop its integration, and theDetector automatically transfers an accumulated image to a frame grabberon the computer (e.g., and repeating steps for remaining spectralchannels in the current spatial position), after which themicrocontroller is operable to start the next cycle of image acquisition(e.g., by signaling movement of the stage to the next position).

In certain embodiments, the system comprises an optical train comprisinga demagnification lens (e.g., to optimize resolution for cytometry andincrease imaging speed).

In another aspect, the invention is directed to a system for automatedidentification and/or recovery (e.g., picking and deposition) ofindividual cells of interest, the system comprising a microscopecomprising a light source, an optical train, and a detector capable ofimaging a deposition-well plate (e.g., the multiscale deposition-wellplate of any one of claims 1 to 7) positioned on a motorized stage(e.g., the system capable of imaging in one or more fluorescent channels(e.g., up to 10, up to 20, up to 30, up to 40, from 10 to 20, from 10 to30, or from 10 to 40)).

In certain embodiments, the processor is operable to perform one or moreof steps (i) to (v) of claim 8. In certain embodiments, the processor isoperable to perform one or more of steps (i) to (xi) of claim 9.

In certain embodiments, the system further comprises elements forhardware triggering of light sources (e.g., as in claim 12).

In certain embodiments, the system is capable of performing an imagingrun with at least 12 channels (e.g., at least 12 channels, at least 16channels, or at least 23 channels) in a total time less than 150 minutes(e.g., less than 100 minutes, less than 75 minutes, less than 50minutes, less than 40 minutes, less than 30 minutes, less than 25minutes, less than 20 minutes, less than 15 minutes, less than 10minutes, or less than 5 minutes), where the total imaged area is atleast 1000 mm².

In another aspect, the invention is directed to a method comprisingusing any one of the above-described systems to perform an imaging runwith at least 12 channels (e.g., at least 12 channels, at least 16channels, or at least 23 channels) in a total time less than 150 minutes(e.g., less than 100 minutes, less than 75 minutes, less than 50minutes, less than 40 minutes, less than 30 minutes, less than 25minutes, less than 20 minutes, less than 15 minutes, less than 10minutes, or less than 5 minutes), where the total imaged area is atleast 1000 mm².

In another aspect, the invention is directed to a method of performingmultispectral cytometry (e.g., multicolor slide cytometry), the methodcomprising performing one or more of steps (i) to (iv) as follows (e.g.,performing any 1, 2, 3, or all 4 of (i) to (iv)) (e.g., wherein all ofthe one or more steps (i) to (iv) are performed in no greater than 30minutes, no greater than 10 minutes, or no greater than 5 minutes for agiven imaging task): (i) performing, by a processor of a computingdevice, automated image calibration (e.g., calibration of image of cellsin a micro-/nano-/pico-well grid) based on one or more of (a) to (d):(a) a raw image, (b) a dark frame, (c) a flat field frame, and (d) anillumination field frame; (ii) performing, by the processor,micro-/nano-/pico-well grid identification; (iii) performing, by theprocessor, cell identification and/or data extraction using one or bothof (e) and (f): (e) a segmentation thresholding technique in which thethreshold is based (e.g., solely) on a distribution of detectedbackground pixels; and (f) a signal-to-noise maximization technique inwhich an aperture (e.g., of from 5 to 8 pixels) is set within a definedcell area to minimize dilution of signal from a decrease of signal nearthe cell periphery and (iv) performing, by the processor, spectralspillover compensation (e.g. using an in silico F-minus one technique,e.g., as described in claim 20).

In another aspect, the invention is directed to a method for performingspectral spillover compensation in multicolor slide cytometry, themethod comprising performing one or more of steps (i) to (xi) as followsusing a processor of a computing device (e.g., performing any 1, 2, 3,4, 5, 6, 7, 8, 9, 10, or all 11 of steps (i) to (xi)): (i) identifyinglocation(s) of one or more beads; (ii) extracting a signal intensity ofeach pixel in each of a plurality of spectral channels (e.g., from 10 to30 spectral channels) for each bead; (iii) creating one or more 3Dprobability matrices relating intensity of signal in the spectralchannel assigned to a fluorophore to the signal in each of the otherchannels; (iv) identifying a location of cells in one or more images;(v) extracting a signal in each of the plurality of spectral channelsfor each cell; (vi) extracting a background signal (e.g., from one ormore areas similar in size to an area from which a cell signal isextracted); (vii) determining an amount of each flurophore on each cellusing one or more average spillover values extracted from the one ormore probability matrices (e.g., and using standard linearcompensation); (viii) creating n-replicas of the compensated fluorophorecontent of each cell (e.g., in each replica, one fluorophore content iszeroed by replacing the value with a sample taken from the backgroundsignal distribution); (ix) sampling at least one of the one or more 3Dprobability matrices to calculate an expected distribution of rawfluorescent signal in each channel based on concentration of eachfluorophore; (x) compensating reconstructed pseudo-raw fluorescentvalues to create a distribution of calculated signal on cells identifiedas having no actual fluorophores present (e.g., population-level insilico FMOs); and (xi) resampling a plurality of times (e.g., from 5 kto 100 k, or from 10 k to 100 k, or from 10 k to 1M times) for each cellto generate an expected negative cell distribution for each individualcell (e.g., single cell in silico FMOs).

In another aspect, the invention is directed to a method for hardwaretriggering of light sources for image acquisition (e.g., in multicolorcytometry, e.g., multicolor slide cytometry), the method comprising:transmitting, by a processor of a computing device, spatial positions toa memory of a Stage, and filter positions to a memory of a Filter Wheel;transmitting, by the processor, one or other parameters of acquisitionto a microcontroller (e.g., wherein the one or more parameter comprisesa number of positions and/or spectral channels to be acquired, one ormore Light Source(s), exposure times, and/or Filter Wheel movements setfor each channel); starting, by the microcontroller, a cycle of imageacquisition by signaling the Stage to move to a next position stored inits memory, wherein the Stage moves to the stored position and signalsthe microcontroller upon completion of the move; signaling, by themicrocontroller, the Filter Wheel to move to the next position stored inits memory, after which the Filter Wheel moves to the stored positionand signals the microcontroller upon completion of the move; signaling,by the microcontroller, the Light Source(s) to turn it/them on, thensignaling a Detector to begin integration of light, and upon completionof exposure time, signaling, by the microcontroller, the Light Source(s)to turn them off; signaling, by the microcontroller, the Detector tostop its integration; automatically transferring an accumulated imagefrom the Detector to a frame grabber (e.g., and repeating steps forremaining spectral channels in the current spatial position); andbeginning, by the microcontroller, a subsequent cycle of imageacquisition (e.g., by signaling movement of the stage to the nextposition).

In another aspect, the invention is directed to a system for automatedidentification and recovery of individual cells of interest, the systemcomprising: a microscope comprising a light source, an optical train,and a detector capable of imaging a deposition—well plate positioned ona motorized stage (e.g., capable of imaging in one or more fluorescentchannels); a motorized stage and a set of actuators configured totranslate the stage in a first direction and a second direction in ahorizontal plane (e.g., wherein translation of the stage is constrainedto an x-y plane); a motorized focus drive to translate an opticalobjective of the microscope in a vertical direction (e.g., z-direction);a micromanipulator arm comprising an actuator configured for constrainedmovement of the micromanipulator arm in the vertical direction (e.g.,z-direction) and optionally in two other dimensions (e.g., x-y plane) tocalibrate a location of a capillary needle within an imaging field ofview of the detector, wherein the capillary needle is removablyfastened/fastenable to the micromanipulator arm and oriented in thevertical direction; optionally, an electronically-controlledmicropumping system (e.g., liquid displacement and other types of pumpsfor manipulation of small volumes of fluid) comprising pumps (e.g.,positive and negative pressure pumps, e.g., displacement pumps, e.g.,velocity, gravity, other actuation types of pumps) and valves (e.g., formanipulating nano- to microliter volumes of fluid, e.g., forintroduction of a volume of fluid comprising each individual cell ofinterest into the capillary needle and/or for release of the volume offluid thereby depositing each individual cell of interest into a firstrecovery well of one or more recovery wells); one or moredeposition-well plates comprising one or more sample wells (e.g., 3-12)and/or one or more recovery wells (e.g., 24, 48, 96, etc.), wherein thedeposition-well plates are removably attached/attachable to themotorized stage; an optional electromechanical arm for automatedintroduction of deposition plates onto the stage; and a processor of acomputing device, wherein the processor is configured to send a seriesof control signals to cause: (i) the microscope to capture an image of afirst sample well, wherein the processor is further configured toanalyze the image to identify a location of an individual cell ofinterest within the first sample well; (ii) the set of actuators totranslate the motorized stage (e.g., in the horizontal plane and themotorized focus drive in the vertical direction) according to theidentified location of the individual cell of interest within the firstsample well, such that the capillary needle is oriented above theindividual cell of interest; (iii) the actuator to translate, in thevertical direction and optionally in the horizontal plane, themicromanipulator arm to orient a tip of the capillary needle in thefirst sample well at or sufficiently near the individual cell ofinterest; (iv) introduction of a volume of fluid comprising theindividual cell of interest into the capillary needle; (v) the actuatorto translate, in the vertical direction and optionally in the horizontalplane, the micromanipulator arm such that the capillary needlecontaining the volume of fluid comprising the individual cell ofinterest is raised out of the first sample well; (vi) the set ofactuators to translate the motorized stage, such that the capillaryneedle containing the volume of fluid comprising the individual cell ofinterest is oriented above the first recovery well; (vii) the actuatorto translate, in the vertical direction and optionally in the horizontalplane, the micromanipulator arm such that the capillary needlecontaining the volume of fluid comprising the individual cell ofinterest is lowered into the first recovery well; and (viii) a releaseof the volume of fluid thereby depositing the individual cell ofinterest into the first recovery well.

In certain embodiments, the capillary needle is (or comprises) steel(e.g., surgical steel, stainless steel, etc.), glass, or plastic. Incertain embodiments, the system further comprises: a back-lightillumination system co-located with the micromanipulator arm andcapillary needle and oriented to project light such that the microscopecollects sufficient transmitted light to image and analyze theindividual cell of interest in this channel.

In certain embodiments, the processor is further configured to perform amulti-point calibration of a surface of the deposition-well plate tocorrect spatial (e.g., rotational or deformational) variations inthree-dimensional space, thereby providing a coordinate system enablingthe microscope stage and the motorized focus drive to be automaticallytranslated by the processor. In certain embodiments, the multi-pointcalibration comprises positioning the motorized stage at positionscorresponding to one or more locations of an imaging region of thedeposition-well plate; identifying coordinates corresponding to theselocations; and using the coordinates to extrapolate one or more pointscorresponding to one or more additional positions within the imagingregion, respectively, thereby correcting for spatial (e.g., rotationalor deformational) variations of the deposition-well plate. In certainembodiments, the processor is configured to perform automated search forspecific points on the deposition-well plate using a software imageanalysis algorithm to detect the specific points. In certainembodiments, the processor is configured to perform multi-pointcalibration of imaging focus at one or more select locations of thedeposition-well plate using a software autofocus algorithm comprising afocus scoring method (e.g., variance of laplacian method or normalizedvariance method) and a one-dimensional root-finding algorithm (e.g.,Brent's minimization algorithm), and extrapolating the multi-pointcalibration for a plurality of other locations of the deposition-wellplate (e.g., performed automatically by the microscope based on one ormore absolute positions of the stage, e.g., post-initialization, e.g.,where position of the deposition-well plate is fixed with respect to thestage).

In certain embodiments, the processor is configured to determine aspatial (e.g., vertical) position of the tip of the capillary needlebased on one or more needle (e.g., position, e.g., height) calibrationimages (e.g., an autofocus image) (e.g., thereby obviating the need fora manual recalibration after a capillary needle change).

In certain embodiments, the introduction of the volume of fluidcomprising the individual cell of interest into the capillary needle andthe release of the individual cell of interest into the first recoverywell are conducted with or without a working fluid (e.g., silicon oil),and/or with or without a micropump. In certain embodiments, theintroduction of the volume of fluid comprising the individual cell ofinterest into the capillary needle and the release of the individualcell of interest into the first recovery well are further controlled bythe processor based on an image analysis algorithm (e.g., aparticle-tracking algorithm) and spatial data structure (e.g., k-d treedata structure) designed to trace locations of individual cells on thefirst recovery well and/or the capillary needle.

In certain embodiments, the individual cell of interest is a memberselected from the group consisting of a circulating tumor cell (CTC), alymphocyte, a leukocyte, a tumor cell, a stromal cell, a neuronal cell,a cell line (e.g., a CHO cell, a NS0 cell), a stem cell, and an embryo.

In certain embodiments, the system comprises a module (“Nanobox”) toautomatically identify candidate individual cells of interest (e.g.,based on morphology and a pre-defined set of fluorescence intensitythresholds), present the images of candidate cells (e.g., in transmittedand fluorescent light channels) to a user for manual review (e.g.,assisted by a machine learning algorithm), and automatically transferthe chosen cells into recovery wells (e.g., at a rate of 100-1000 cellsper hour) (e.g., one cell at a time). In certain embodiments, the systemis further configured to: detect and present dynamic behaviors ofindividual cells of interest based on images taken at multiple timepoints; trace the locations of individual cells of interest over time(including time to transfer the chosen cells into recover wells); andresolve potential duplicates amongst candidate cells of interest due toan overlap between adjacent images (e.g., using an image analysisalgorithm and k-d tree algorithm).

In certain embodiments, the processor is further configured with amodule (e.g., a built-in module or a stand-alone module) to define anoptimal set of fluorescence intensity thresholds based on statisticaland/or visual analysis performed simultaneously with loading andprocessing of images; and/or to simultaneously present the images underscreen cursor in all channels and mark by a user locations of truepositive individual cells of interest that are either detected correctlyor missed by the processor (e.g., to facilitate transfer of chosen cellsof interest into recovery wells, e.g., to teach a machine learningalgorithm to suggest individual cells of interest).

In another aspect, the invention is directed to a method of enriching acell type of interest, the method comprising: processing (e.g., lysing)a sample of biological fluid (e.g., blood, plasma, urine, sputum,saliva, amniotic fluid, cerebrospinal fluid, etc.), thereby forming acell suspension; incubating the cell suspension with immunomagneticbeads configured to selectively label a cell type of interest (e.g.,circulating tumor cells), thereby forming a biological fluid comprisinga bead-labeled cell suspension; dispensing the biological fluid upon abiocompatible dense medium (e.g., Percoll™, e.g., a colloidal suspensionof silica nanoparticles coated with polyvinylpyrrolidone), therebyforming a layered fluid comprising a biological fluid layer and abiocompatible dense medium layer; causing the labeled cells to settleinto the biocompatible dense medium layer (e.g., waiting sufficient timefor gravity to settle the labeled cells, e.g. placing a magnetunderneath the biocompatible dense medium); and aspirating thebiological fluid layer (e.g., substantially removing the cells of thebiological fluid which are not the cell type of interest.

In certain embodiments, causing the labeled cells to settlesubstantially beneath the biocompatible dense medium layer comprisesintroducing a magnetic field beneath the biocompatible dense mediumlayer, thereby forcing the labeled cells into the biocompatible densemedium layer (e.g., down to a bottom of a recovery well containing thebiological fluid layer and the biocompatible dense medium layer). Incertain embodiments, the method comprises washing and staining thelabeled cells.

In certain embodiments, the biological fluid is a member selected fromthe group consisting of blood, plasma, urine, sputum, saliva, amnioticfluid, bone marrow aspirate, fine-needle aspirate (e.g., or other smalltissue biopsies), whipple, and cerebrospinal fluid. In certainembodiments, the biological fluid is blood and the cell type of interestis circulating tumor cells.

In another aspect, the invention is directed to a method for automatedidentification and recovery of individual cells of interest, the methodcomprising: capturing, by a detector (e.g., a detector of a microscope),an image of a first sample well; analyzing, by a processor of acomputing device, the image to identify a location of an individual cellof interest within the first sample well; automatically translating amotorized stage in a first direction and/or a second direction in ahorizontal plane (e.g., the x-y plane), and a focus drive in a thirddirection perpendicular to a horizontal plane (e.g., the z plane),according to the identified location of the individual cell of interestwithin the first recovery well, such that a capillary needle removablyfastened to a micromanipulator arm is oriented above the individual cellof interest; automatically translating, in a vertical direction, andoptionally in the horizontal plane, the micromanipulator arm to orient atip of the capillary needle in the first sample well at or sufficientlynear the individual cell of interest; introducing a volume of fluidcomprising the individual cell of interest into the capillary needle;automatically translating, in the vertical direction, themicromanipulator arm such that the capillary needle containing thevolume of fluid comprising the individual cell of interest is raised outof the first sample well; translating the microscope stage, such thatthe capillary needle containing the volume of fluid comprising theindividual cell of interest is oriented above a first recovery well;automatically translating, in the vertical direction and optionally inthe horizontal plane, the micromanipulator arm to orient a tip of thecapillary needle at a sufficient height above the bottom of the firstrecovery well; and releasing the volume of fluid thereby depositing theindividual cell of interest into the first recovery well.

In certain embodiments, the method further comprises detecting andpresenting dynamic behaviors of individual cells of interest based onimages taken at multiple time points; tracing the locations ofindividual cells of interest over time (including time to transfer thechosen cells into recovery wells); and resolving potential duplicatesamongst candidate cells of interest due to an overlap between adjacentimages (e.g., using an image analysis algorithm and k-d tree algorithm).

In certain embodiments, the method further comprises: defining anoptimal set of fluorescence intensity thresholds based on statisticaland/or visual analysis performed simultaneously with loading andprocessing of images; and/or simultaneously presenting the images underscreen cursor in all channels and marks by a user locations of truepositive individual cells of interest that are either detected correctlyor missed by the processor (e.g., to facilitate transfer of chosen cellsof interest into recovery wells, e.g., to teach a machine learningalgorithm to suggest individual cells of interest).

In certain embodiments, the detector (e.g., microscope) and/or theprocessor comprises a module for: automatically scanning (e.g., using alow-magnification objective) and register 1- and/or 2-dimensionalbarcodes printed by a plate manufacturer at the bottom and/or a sidewall of the recovery wells and/or recovery well plates; associatingindividual cells of interest with such wells and plates; and storingsuch associations either directly in or in a format compatible with adatabase.

In another aspect, the invention is directed to a deposition-well platecomprising one or more sample wells (e.g., 3-12) and one or morerecovery wells (e.g., 24, 48, 96, etc.).

In certain embodiments, the deposition-well plate additionally comprisesone or more (e.g., 1-3) washing wells (e.g., containing a washing orlysis buffer) (e.g., to wash the tip of the capillary needle betweeneach cell picking event). In certain embodiments, the one or morewashing wells are located on a separate dedicated well-plate orintegrated as part of the system.

In certain embodiments of the above-described systems, the processor isconfigured to automatically determine a success of transfer of cellsusing an image processing algorithm performed on images taken before andafter handling of cells with the capillary needle.

It is contemplated that where embodiments are described with respect toone aspect of the invention, they may also apply with respect to otheraspects of the invention.

Definitions

“Detector”: As used herein, the term “detector” includes any detector ofelectromagnetic radiation including, but not limited to, EMCCD camera,CMOS camera, photomultiplier tubes, photodiodes, and avalanchephotodiodes.

“Substantially”: As used herein, the term “substantially”, andgrammatical equivalents, refer to the qualitative condition ofexhibiting total or near-total extent or degree of a characteristic orproperty of interest. One of ordinary skill in the art will understandthat biological and chemical phenomena rarely, if ever, go to completionand/or proceed to completeness or achieve or avoid an absolute result.

Figures are presented herein for illustration purposes only, not forlimitation.

It is contemplated that methods, systems, and processes described hereinencompass variations and

Drawings are presented herein for illustration purposes, not forlimitation.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other objects, aspects, features, and advantages ofthe present disclosure will become more apparent and better understoodby referring to the following description taken in conduction with theaccompanying drawings, in which:

FIG. 1A shows compartmentalization of multiple samples on a singleplastic device using an array of macroscopic wells (“macrowells”).

FIG. 1B shows arrays of microscopic wells (henceforth referred to as“nanowells” because of their sub-nanoliter volume) arranged in a gridmatched to the arrangement of the macroscopic wells.

FIG. 1C shows that each array of nanowells resides at the bottom of amacrowell and allows for microscopic compartmentalization of cells fromthe corresponding sample.

FIGS. 2A-2B show microfabrications of nanowells with tapered walls thatis optimal for cell recovery and manufacturing, as compared to standardnanowells with vertical walls.

FIG. 3 shows an exemplary workflow using deposition plates for cellisolation, imaging, identification, and recovery.

FIG. 4 shows a method for on-plate enrichment that combinesimmunomagnetic labeling with density gradients.

FIG. 5 shows a deposition well-plate that is manufactured such thatsample wells and recovery wells are positioned closely. This well-platesolves the problem of distant movements among all of these stations(nanowells, receiving wells, wash wells). Existing solutions (e.g.,CellCelector, micromanipulators) have separate stations that requiredistant travel from one location to the other.

FIG. 6 shows an exemplary embodiment in which a deposition well-platecan be analyzed.

FIG. 7 is a block diagram of an example network environment for use inthe methods and systems for analysis of spectrometry data, according toan illustrative embodiment.

FIG. 8 is a block diagram of an example computing device and an examplemobile computing device, for use in illustrative embodiments of theinvention.

FIG. 9A is a flow chart 900 depicting steps performed by an exemplarynanobox module, according to an illustrative embodiment. As the term isused herein, “nanobox” refers to a module (e.g., set of instructions)for execution by a processor of a computing device, in particular, incertain embodiments, a cytometry module for performing a cytometryprocedure. Step 902 is automated image calibration, step 904 is nanowellgrid identification, step 906 is cell identification, step 908 is dataextraction, step 910 is spectral spillover compensation, and step 912 isin silico F-minus one generation.

FIG. 9B depicts further detail involved in the in silico F-minus onegeneration step 912.

FIG. 10 shows 3D spectral probability matrices of fluorophores detectedin channels. The x axis shows the amount of fluorophore. The y axisshows the amount of signal in incorrect spectral channel. The z axisshows the probability and spillover intensity.

FIG. 11 shows a flow chart of generating 3D spectral probabilitymatrices.

FIG. 12 shows hardware triggering of an exemplary imaging system.

FIG. 13 shows a schematic of a microscope design as described herein.

FIG. 14A shows a scatter plot of beads that were labeled individuallywith 11 different fluorophores and imaged using the triggering proceduredescribed herein. The intensity of fluorescence after compensation forall beads is plotted. Each bead displays signal in only a single channel(all beads align along the axes of each plot).

FIG. 14B shows cells that were labeled with fluorescent markers andimaged on the Present system using the triggering procedure, asdescribed herein, at a speed of 60 mm²/min for 12 channels. Hierarchicalgating of the data after data extraction and compensation is displayedfor some common cell phenotypes.

FIG. 15 shows plots of fluorescent intensities of cells extracted from aset of images by nanobox and in silico FMO.

DETAILED DESCRIPTION

Throughout the description, where compositions are described as having,including, or comprising specific components, or where methods aredescribed as having, including, or comprising specific steps, it iscontemplated that, additionally, there are compositions of the presentinvention that consist essentially of, or consist of, the recitedcomponents, and that there are methods according to the presentinvention that consist essentially of, or consist of, the recitedprocessing steps.

It should be understood that the order of steps or order for performingcertain action is immaterial so long as the invention remains operable.Moreover, two or more steps or actions may be conducted simultaneously.

The mention herein of any publication, for example, in the Backgroundsection, is not an admission that the publication serves as prior artwith respect to any of the claims presented herein. The Backgroundsection is presented for purposes of clarity and is not meant as adescription of prior art with respect to any claim.

Headers are used herein to aid the reader and are not meant to limit theinterpretation of the subject matter described.

Described herein are systems and methods for automatically identifyingand recovering individual cells of interest from a sample of biologicalmatter, e.g., a biological fluid, tumor biopsies from fine needleaspirates, cerebrospinal fluid, blood, sputum, saliva, amniotic fluid,cerebrospinal fluid, urine, etc. Also described are methods of enrichinga cell type of interest. Cells of interest include, but are not limitedto lymphocytes, leukocytes tumor cells, stromal cells, neuronal cells,cell lines (like CHO cells, NS0 cells), stem cells, embryos, and thelike.

These systems and methods offer advantages over pre-existing systems inthat they allow automated (or semi-automated) identification andrecovery of individual cells at a high throughput. The systems andmethods also allow for manual verification of automatically-identifiedcandidate cells, which may be advantageous for satisfaction of certainregulatory requirements, while still allowing for high throughput.Moreover, the systems and methods described herein allow for coordinatedperformance of two or more of the following, e.g., all with the samedevice: cell enrichment, cell identification, and individual cellrecovery for further analysis (e.g., sequencing) of individual,recovered cells.

Further advantages compared to conventional methods include (1)maintenance of viability of small clinical samples (e.g., samples arenot dissolved or degraded during measurements; cells are not destroyed,permeabilized or rendered non-viable), (2) increased sensitivity due toviability, and (3) ability to perform additional measurements toaggregate data (e.g., kinetics, cytolysis, motility, proliferativecapacity, growth, secretion, functional assays) to determine which cellsare of interest.

In one example, circulating tumor cells (CTCs) are rare tumor cellsfound in the blood of cancer patients (˜1 ppm mononuclear cells) and arebelieved to be responsible for disseminating cancer (metastasis). Thenumbers of CTCs found in blood can serve as a prognostic indicator incertain tumor types. CTCs offer many opportunities beyond enumeration.Indeed, molecular analysis of CTCs may reveal information about solidtumor lesions and allow monitoring of the progression of disease fromblood samples. Along with the analysis of circulating tumor DNA, such“liquid biopsies” offer a real-time, minimally-invasive window intometastasis that would not be feasible using repeated surgical biopsies.

Sequencing-based analyses of CTCs allow the tracing of lineage-specificevolution of tumors, assessment of clonal heterogeneity, andidentification of mechanisms of resistance to therapies. However, theinherently small amount of material available from each cell (e.g., only1 copy of each parental allele) necessitates the use of amplificationmethods prior to sequencing that introduce biases and errors whichconfound the confident calling of mutations and copy-number alterations.Census-based methods enable accurate and powered calling of somaticalterations from CTCs, but require isolating, amplifying, and sequencingmultiple independent CTCs, and thus are limited when an insufficientnumber of cells is available. As such, increasing the number of singleCTCs recovered from a given volume of blood (or processing largervolumes in a given time) is paramount to performing more confident anddetailed analyses, along with expanding the (sometimes incompatible)types of analysis performed on each sample.

Deposition Wells

Arrays of micro-, nano-, or pico-wells offer a solution forcompartmentalization of single cells for high-content multi-dimensionalanalysis of such samples. This technology enables correlation ofmultiple measurements involving both phenotype and genotype that can beperformed either on-chip or off-chip in a single pipeline, establishinga platform for integrative single-cell analysis. Presented herein is amicro-, nano-, or pico-well platform for high-throughput cytometry andrecovery of rare single cells that offers significant improvements inall these directions and has direct implications for preclinical andclinical research.

To accommodate increased numbers of samples processed in each modularoperation, and expand compatibility with automated equipment used forthese operations (e.g., liquid handlers, robotic macro- andmicromanipulators, optical equipment, etc.), a new array is presentedherein that relies on the form factor of the microtiter plates. Thisstandard—SBS format (named after the Society for BiomolecularScreening)—provides compartmentalization of multiple samples on a singleplastic device using an array of macroscopic wells (“macrowells”) (FIG.1A).

In certain embodiments, the second part of the device comprises arraysof microscopic wells (henceforth referred to as “nanowells” because oftheir sub-nanoliter volume) arranged in a grid matched to thearrangement of the macroscopic wells (FIG. 1B). Each array of nanowellsresides at the bottom of a macrowell and allows for microscopiccompartmentalization of cells from the corresponding sample (FIG. 1C).These wells can comprise glass, silicon, polymers (polycarbonate,polystyrene, epoxy, ABS plastics, polypropylene, fluoropolymers),elastomers (polydimethylsiloxane), thermoplastics, or other commonmedical grade plastics or other materials compatible with cells.

Both parts of the device can be manufactured independently of eachother, allowing additional flexibility not readily achieved with asingle-piece geometry. It is possible to ensure that each part isreliably manufactured according to its specific set of dimensions andtolerances, which are vastly different for the macro- and themicroscale. To demonstrate the utility of these devices prior to massmanufacturing, a number of rapid prototyping tools can be used that aresuitable for the production of each part. These tools allow one toproduce dozens of devices in rapid iterations to evaluate and achieve anoptimal set of geometries. For example, 3D printing andstereolithography (SLA) are fast, accessible, and relatively inexpensiveadditive technologies for building macroscopic pieces where highresolution is not required. The microscopic part can be replicated, forexample, with soft embossing from a rigid master that is fabricated oncein a standard cleanroom facility.

In certain embodiments, anisotropic etching of Si can be used for themicrofabrication of a master for molding nanowells with tapered walls.This geometry facilitates replication of the rigid master in asubsequent molding step, as the draft angle of ˜35° (defined bycrystalline planes in Si), along with an extremely low surface roughnessproduced by the process, aid separation of the molded piece from themaster (FIG. 2A). Additionally, such wells can be moved closer to eachother, while maintaining the overall thickness and rigidity of the wallsin between, which may otherwise deform or collapse upon replication inplastic had the walls been vertical (e.g. for 10 μm nominal width and20-35 μm depth of the walls); see FIG. 2B for comparison. Closeseparation between the wells maximizes the useful area of the device(allowing more wells to fit in a smaller area), increasing the number ofsamples that can be processed on each device, decreasing imaging timesper sample and minimizing cell loss due to the “dead” area (so that alarger proportion of cells could settle into the wells from suspension).In certain embodiments, bulk micromachining of Si is employed, ensuringthat microscopic features are a single piece with the wafer, unlike inprocesses where the features are attached to the wafer and may wear outwith repeated moldings (an example of such process is patterning of SU-8posts). Other bulk micromachining methods may be employed (e.g., deepreactive-ion etching—DRIE), provided they are optimized for taperedwalls (but may have a different angle).

In certain embodiments, to facilitate image acquisition and analysis,the nanowells are arranged in blocks that fit the field of view on themicroscope; if the blocks were spaced further apart, the separation areawould be unused in cell loading (potentially resulting in cell loss),while a shorter distance between the blocks would increase the number ofblocks per given area, thus increasing the time spent on imaging and therisk of overexposure of sensitive fluorophores. As many wells aspossible may be fit within each block, while accounting for a possibledrift of the microscope stage from one block to another, which can leadto incomplete imaging of the outermost wells.

In certain embodiments, to reduce cell loss, the edges of each array ofnanowells (corresponding to one macrowell) are designed to be as closeas possible to the edges of the macrowell. However, since the two partsof the final device have different tolerances on the materials andprocesses involved in their production, and they have to be aligned andbonded, it is not always possible to rely on a microscopically precisealignment. As such, in certain embodiments, a ˜200 μm border gap isintroduced at the edges of each array. Additionally, to be able toaccess the outermost wells with macroscopic tools such as a liquidhandling pipette or a cell picking capillary, as well as to facilitatemolding of the macrowell plate in the future, a taper may be introducedto the sidewalls of each macrowell. In certain embodiments, macrowellsand corresponding grids of blocks of nanowells may have geometries otherthan rectangular (e.g., circular) to improve compatibility with existingmaterials and methods specified by the SBS standards (e.g., those for 24or 96-well plates). In certain embodiments geometries other than thosecompatible with standard 24-well or 96-well flat bottom plates are used.

Workflow

An exemplary workflow for nanowell devices is illustrated in FIG. 3. Theworkflow starts with fluorescently labeling a suspension of cells drawnfrom a clinical sample (this could be a disaggregated tumor, a tissuebiopsy, whole blood, etc.). The clinical sample may be optionallypre-enriched for the cells of interest by application of an externalmethod (e.g. immunomagnetic enrichment or label-free sorting). Cells arethen loaded from each suspension into the bottom of a correspondingmacrowell (and optionally, into the nanowells at the bottom of themacrowells), which is accomplished in any of a number of ways (e.g.,settling by gravity, centrifugation, immunomagnetic enrichment etc.).The workflow then proceeds with automated imaging of each well in ablock-by-block fashion, and the results are processed with automated orsemi-automated image analysis software (e.g., using the cytometry moduleby the processor). This analysis automatically produces a list ofcandidate cells (e.g., requiring manual confirmation), along with theirassociated locations, which can then be used to transfer individualcells of interest to the wells of a multiwell deposition plate (e.g., a96-well plate) for downstream analysis (e.g., biochemical analysis,metabolites, transcriptional profiling, targeted sequencing, single-cellRNA sequencing, whole exome sequencing, epigenetic measurements (e.g.,methylation, histones, chromatin), whole genome sequencing, or clonalexpansion).

Immunomagnetic Enrichment

Various cells of interest may be identified and recovered, such ascirculating tumor cells (CTCs). After the sample is enriched and cellsare identified, they may still need to be recovered for furtheranalysis. The new format of the deposition plates is leveraged tointegrate enrichment with the automated platform established for cellidentification and recovery.

A method for on-plate enrichment is presented that combinesimmunomagnetic labeling with density gradients (FIG. 4). Lysis ofcontaminating cells such as red blood cells (RBCs) is performed on avial of blood, and the resulting cell suspension is then incubated withimmunomagnetic beads to selectively label selection marker-expressingcells (e.g., EpCAM-expressing CTCs). Macrowells of the plates are filledwith a biocompatible dense medium such as Percoll™ (a colloidalsuspension of silica nanoparticles coated with polyvinylpyrrolidone),and the bead-labeled cell suspension is dispensed on top of it; the twoliquids remain separated according to their densities. Since the beadsare much heavier than the cells or the medium (e.g., iron has 6-7 timeshigher density), and a few of them are conjugated to each cell, they canpull labeled cells through the dense layer by gravity. Additionally (oralternatively), these cells can be pulled with a magnet placedunderneath the imaging surface (FIG. 4). As a result, labeled cellssediment onto the imaging surface of the deposition plate, but at most afew non-specific contaminating cells such as white blood cells (WBCs)ever reached the same level.

After sedimentation, the blood layer and the dense medium are aspirated,which removes the majority of contaminating cells. The next step is towash, stain, and image cells on the same surface (FIG. 4, right). Cellssuch as EpCAM-expressing CTCs selected based on positive and negativefluorescent markers and morphology are subsequently recovered byautomated micromanipulation to a deposition plate (e.g., a PCR plate ormicrotiter plate or strip of PCR tubes) for subsequent analysis (such aswhole-genome amplification, library preparation, and sequencing). Thisformat allows direct performance of cell enrichment on deposition platesusing the macrowells to partition the samples.

Multi-Spectral Image Cytometry

Multi-Spectral Image Cytometry (MuSIC) uses an epi fluorescencemicroscope to access 16 phenotypic parameters simultaneously on 1,000 or10,000 or 100,000 or 1,000,000 single cells. Substantially all cellsremain viable and individually accessible for downstream analyses (e.g.,biochemical analysis, metabolites, transcriptional profiling, targetedsequencing, single-cell RNA sequencing, whole exome sequencing,epigenetic measurements (e.g, methylation, histones, chromatin), wholegenome sequencing, or clonal expansion).

The increased throughput of the new design enables more efficientprocessing of a greater number of samples, allowing scale-up ofsingle-cell analyses not possible with previous designs. SBS format isused for automated high-throughput screening assays in the industry; thesystems and methods presented herein have now employed the SBS format tophenotypic analyses at the single-cell level that preserve viability,identity, and potential for recovery of cells throughout an experiment.By compartmentalizing cells in the nanowells (a unique feature of assayspresented herein), it is possible to improve isolation and simplifylocalization and recovery of individual cells of interest.

In certain embodiments, a multichannel beamsplitter is used to image thesame block of wells (illuminated with a single excitation source) inmultiple emission bands simultaneously using a single detector (e.g.,with sufficiently large sensor area to accommodate multiple channels) tospeed up total imaging time and reduce exposure of sensitivefluorophores to the corresponding excitation.

Exemplary fluorophores that can be used include, but are not limited to,the following: Calcein Violet, Alexa Fluor 488, BV605, PE, Alexa Fluor568, BV650, PerCP, PE-Cys, Alexa Fluor 647, BV711, PerCP-eFluor710,PE-Alexa Fluor 700, BV786, PE-Cy7, APC-Cy7, and brilliant violet dye(e.g., BV711, BV786, BUV395, BUV496, BUV661, BUV737, BUV805, BB515,BV421, BV510, BV605, BV650, BV711, BV786), 1,5 IAEDANS; 1,8-ANS;4-Methylumbelliferone; 5-carboxy-2,7-dichlorofluorescein;5-Carboxyfluorescein (5-FAM); 5-Carboxynapthofluorescein;5-Carboxytetramethylrhodamine (5-TAMRA); 5-FAM (5-Carboxyfluorescein);5-HAT (Hydroxy Tryptamine); 5-Hydroxy Tryptamine (HAT); 5-ROX(carboxy-X-rhodamine); 5-TAMRA (5-Carboxytetramethylrhodamine);6-Carboxyrhodamine 6G; 6-CR 6G; 6-JOE; 7-Amino-4-methylcoumarin;7-Aminoactinomycin D (7-AAD); 7-Hydroxy-4-methylcoumarin;9-Amino-6-chloro-2-methoxyacridine; ABQ; Acid Fuchsin; ACMA(9-Amino-6-chloro-2-methoxyacridine); Acridine Orange; Acridine Red;Acridine Yellow; Acriflavin; Acriflavin Feulgen SITSA; Aequorin(Photoprotein); AFPs—AutoFluorescent Protein—(Quantum Biotechnologies)see sgGFP, sgBFP; Alexa Fluor 350™; Alexa Fluor 430™; Alexa Fluor 488™;Alexa Fluor 532™; Alexa Fluor 546™; Alexa Fluor 568™; Alexa Fluor 594™;Alexa Fluor 633™; Alexa Fluor 647™; Alexa Fluor 660™; Alexa Fluor 680™;Alizarin Complexon; Alizarin Red; Allophycocyanin (APC); AMC, AMCA-S;AMCA (Aminomethylcoumarin); AMCA-X; Aminoactinomycin D; Aminocoumarin;Aminomethylcoumarin (AMCA); Anilin Blue; Anthrocyl stearate; APC(Allophycocyanin); APC-Cy7; APTRA-BTC; APTS; Astrazon Brilliant Red 4G;Astrazon Orange R; Astrazon Red 6B; Astrazon Yellow 7 GLL; Atabrine;ATTO-TAG™ CBQCA; ATTO-TAG™ FQ; Auramine; Aurophosphine G; Aurophosphine;BAO 9 (Bisaminophenyloxadiazole); BCECF (high pH); BCECF (low pH);Berberine Sulphate; Beta Lactamase; BFP blue shifted GFP (Y66H); BlueFluorescent Protein; BFP/GFP FRET; Bimane; Bisbenzamide; Bisbenzimide(Hoechst); bis-BTC; Blancophor FFG; Blancophor SV; BOBO™-1; BOBO™-3;Bodipy 492/515; Bodipy 493/503; Bodipy 500/510; Bodipy 505/515; Bodipy530/10; Bodipy 542/563; Bodipy 18/568; Bodipy 564/517; Bodipy 576/589;Bodipy 581/591; Bodipy 630/650-X; Bodipy 650/665-X; Bodipy 665/676;Bodipy FI; Bodipy FL ATP; Bodipy FI-Ceramide; Bodipy R6G SE; Bodipy TMR;Bodipy TMR-X conjugate; Bodipy TMR-X, SE; Bodipy TR; Bodipy TR ATP;Bodipy TR-X SE; BO-PRO™-1; BO-PRO™-3; Brilliant Sulphoflavin FF; BTC;BTC-5N; Calcein; Calcein Blue; Calcium Crimson™; Calcium Green; CalciumGreen-1 Ca.sup.2+ Dye; Calcium Green-2 Ca.sup.2+; Calcium Green-5NCa.sup.2+; Calcium Green-C18 Ca.sup.2+; Calcium Orange; CalcofluorWhite; Carboxy-X-rhodamine (5-ROX); Cascade Blue™; Cascade Yellow;Catecholamine; CCF2 (GeneBlazer); CFDA; CFP—Cyan Fluorescent Protein;CFP/YFP FRET; Chlorophyll; Chromomycin A; Chromomycin A; CL-NERF; CMFDA;Coelenterazine; Coelenterazine cp; Coelenterazine f; Coelenterazine fcp;Coelenterazine h; Coelenterazine hcp; Coelenterazine ip; Coelenterazinen; Coelenterazine O; Coumarin Phalloidin; C-phycocyanine; CPMMethylcoumarin; CTC; CTC Formazan; Cy2™; Cy3.1 8; Cy3.5™; Cy3™; Cy5.1 8;Cy5.5™; Cy5™; Cy7™; Cyan GFP; cyclic AMP Fluorosensor (FiCRhR); Dabcyl;Dansyl; Dansyl Amine; Dansyl Cadaverine; Dansyl Chloride; Dansyl DHPE;Dansyl fluoride; DAPI; Dapoxyl; Dapoxyl 2; Dapoxyl 3′ DCFDA; DCFH(Dichlorodihydrofluorescein Diacetate); DDAO; DHR (Dihydorhodamine 123);Di-4-ANEPPS; Di-8-ANEPPS (non-ratio); DiA (4-Di-16-ASP);Dichlorodihydrofluorescein Diacetate (DCFH); DiD-Lipophilic Tracer; DiD(DiIC18(5)); DIDS; Dihydorhodamine 123 (DHR); Dil (Di1C18(3));Dinitrophenol; DiO (DiOC18(3)); DiR; DiR (DiIC18(7)); DM-NERF (high pH);DNP; Dopamine; DsRed; DTAF; DY-630-NHS; DY-635-NHS; EBFP; ECFP; EGFP;ELF 97; Eosin; Erythrosin; Erythrosin ITC; Ethidium Bromide; Ethidiumhomodimer-1 (EthD-1); Euchrysin; EukoLight; Europium (III) chloride;EYFP; Fast Blue; FDA; Feulgen (Pararosaniline); FIF (Formaldehyd InducedFluorescence); FITC; Flazo Orange; Fluo-3; Fluo-4; Fluorescein (FITC);Fluorescein Diacetate; Fluoro-Emerald; Fluoro-Gold(Hydroxystilbamidine); Fluor-Ruby; FluorX; FM 1-43™; FM 4-46; Fura Red™(high pH); Fura Red™/Fluo-3; Fura-2; Fura-2/BCECF; Genacryl BrilliantRed B; Genacryl Brilliant Yellow 10GF; Genacryl Pink 3G; Genacryl Yellow5GF; GeneBlazer (CCF2); GFP (S65T); GFP red shifted (rsGFP); GFP wildtype, non-UV excitation (wtGFP); GFP wild type, UV excitation (wtGFP);GFPuv; Gloxalic Acid; Granular blue; Haematoporphyrin; Hoechst 33258;Hoechst 33342; Hoechst 34580; HPTS; Hydroxycoumarin; Hydroxystilbamidine(FluoroGold); Hydroxytryptamine; Indo-1, high calcium; Indo-1, lowcalcium; Indodicarbocyanine (DiD); Indotricarbocyanine (DiR); IntrawhiteCf; JC-1; JO-JO-1; JO-PRO-1; LaserPro; Laurodan; LDS 751 (DNA); LDS 751(RNA); Leucophor PAF; Leucophor SF; Leucophor WS; Lissamine Rhodamine;Lissamine Rhodamine B; Calcein/Ethidium homodimer; LOLO-1; LO-PRO-1;Lucifer Yellow; Lyso Tracker Blue; Lyso Tracker Blue-White; Lyso TrackerGreen; Lyso Tracker Red; Lyso Tracker Yellow; LysoSensor Blue;LysoSensor Green; LysoSensor Yellow/Blue; Mag Green; Magdala Red(Phloxin B); Mag-Fura Red; Mag-Fura-2; Mag-Fura-5; Mag-Indo-1; MagnesiumGreen; Magnesium Orange; Malachite Green; Marina Blue; Maxilon BrilliantFlavin 10 GFF; Maxilon Brilliant Flavin 8 GFF; Merocyanin;Methoxycoumarin; Mitotracker Green FM; Mitotracker Orange; MitotrackerRed; Mitramycin; Monobromobimane; Monobromobimane (mBBr-GSH);Monochlorobimane; MPS (Methyl Green Pyronine Stilbene); NBD; NBD Amine;Nile Red; Nitrobenzoxadidole; Noradrenaline; Nuclear Fast Red; NuclearYellow; Nylosan Brilliant lavin EBG; Oregon Green; Oregon Green 488-X;Oregon Green™; Oregon Green™ 488; Oregon Green™ 500; Oregon Green™ 514;Pacific Blue; Pararosaniline (Feulgen); PBFI; PE-Cy5; PE-Cy7; PerCP;PerCP-Cy5.5; PE-TexasRed [Red 613]; Phloxin B (Magdala Red); PhorwiteAR; Phorwite BKL; Phorwite Rev; Phorwite RPA; Phosphine 3R; PhotoResist;Phycoerythrin B [PE]; Phycoerythrin R [PE]; PKH26 (Sigma); PKH67; PMIA;Pontochrome Blue Black; POPO-1; POPO-3; PO-PRO-1; PO-PRO-3; Primuline;Procion Yellow; Propidium Iodid (PI); PyMPO; Pyrene; Pyronine; PyronineB; Pyrozal Brilliant Flavin 7GF; QSY 7; Quinacrine Mustard; Red 613[PE-TexasRed]; Resorufin; RH 414; Rhod-2; Rhodamine; Rhodamine 110;Rhodamine 123; Rhodamine 5 GLD; Rhodamine 6G; Rhodamine B; Rhodamine B200; Rhodamine B extra; Rhodamine BB; Rhodamine BG; Rhodamine Green;Rhodamine Phallicidine; Rhodamine Phalloidine; Rhodamine Red; RhodamineWT; Rose Bengal; R-phycocyanine; R-phycoerythrin (PE); rsGFP; S65A;S65C; S65L; S65T; Sapphire GFP; SBFI; Serotonin; Sevron Brilliant Red2B; Sevron Brilliant Red 4G; Sevron Brilliant Red B; Sevron Orange;Sevron Yellow L; sgBFP™; sgBFP™ (super glow BFP); sgGFP™; sgGFP™ (superglow GFP); SITS; SITS (Primuline); SITS (Stilbene IsothiosulphonicAcid); SNAFL calcein; SNAFL-1; SNAFL-2; SNARF calcein; SNARF1; SodiumGreen; SpectrumAqua; SpectrumGreen; SpectrumOrange; Spectrum Red; SPQ(6-methoxy-N-(3-sulfopropyl)quinolinium); Stilbene; Sulphorhodamine Bcan C; Sulphorhodamine Extra; SYTO 11; SYTO 12; SYTO 13; SYTO 14; SYTO15; SYTO 16; SYTO 17; SYTO 18; SYTO 20; SYTO 21; SYTO 22; SYTO 23; SYTO24; SYTO 25; SYTO 40; SYTO 41; SYTO 42; SYTO 43; SYTO 44; SYTO 45; SYTO59; SYTO 60; SYTO 61; SYTO 62; SYTO 63; SYTO 64; SYTO 80; SYTO 81; SYTO82; SYTO 83; SYTO 84; SYTO 85; SYTOX Blue; SYTOX Green; SYTOX Orange;Tetracycline; Tetramethylrhodamine (TRITC); Texas Red™; Texas Red-X™conjugate; Thiadicarbocyanine (DiSC3); Thiazine Red R; Thiazole Orange;Thioflavin 5; Thioflavin S; Thioflavin TCN; Thiolyte; Thiozole Orange;Tinopol CBS (Calcofluor White); TMR; TO-PRO-1; TO-PRO-3; TO-PRO-5;TOTO-1; TOTO-3; TriColor (PE-Cy5); TRITC TetramethylRodaminelsoThioCyanate; True Blue; TruRed; Ultralite; Uranine B; Uvitex SFC; wtGFP; WW 781; X-Rhodamine; XRITC; Xylene Orange; Y66F; Y66H; Y66W; YellowGFP; YFP; YO-PRO-1; YO-PRO-3; YOYO-1; YOYO-3, Sybr Green, Thiazoleorange (interchelating dyes), semiconductor nanoparticles such asquantum dots, or caged fluorophore (which can be activated with light orother electromagnetic energy source) and/or combinations thereof.

In certain embodiments, several detectors simultaneously capturing eachfield of view (in different channels) can further decrease total imagingtime and exposure. In certain embodiments, the use of a fast andsensitive detector (e.g., sCMOS) with a high pixel-count (e.g.,2048×2048) and a large (e.g., close to ¾″ diagonal) sensor area incombination with a suitably low magnification (e.g., 4×/0.2NA) furtherfacilitates simultaneous high-content and high-throughput imaging. Insome embodiments, several imagers act in parallel on separate macrowellsto speed up imaging time. In various embodiments, post-processingaccounts for shading in nonhomogenous illumination profiles, either insoftware or hardware. In certain embodiments, microengraving captureselements of the secretome of each cell (in addition to phenotypic traitsfrom cytometry), which greatly expands the amount of information andtypes of assays available for each cell.

Although exemplary embodiment analyses have been demonstrated to besuitable for CTCs and lymphocytes from mucosal samples, in certainembodiments the approach is applied to any area where identification andrecovery of rare cells, or characterization of cellular heterogeneity,are needed, while only small numbers of cells may be available; it couldequally apply to stem cells, bacteria and yeast, or highly heterogeneoustumors. In some embodiments, the current disclosure enables simultaneoushigh-content and high-throughput phenotypic characterization of singlecells, while preserving cell viability and identity for furtheranalyses. The format also allows perturbations of the cells by theaddition of drugs, immunomodulators, antagonists, viruses, and the like.Along with standardization of the form-factor, this makes our approachmodular enough to be integrated with existing analytical pipelines(e.g., ones comprising liquid handlers, automated incubators,sequencers, and the like) to enable an even greater degree ofcharacterization of cells and ultimately improve understanding ofbiological processes and assist diagnostics.

Identification and Collection

In the exemplary embodiments of FIG. 6, a deposition well-plate (FIG. 5)is manufactured such that sample wells and recovery wells are positionedclosely. The close positioning enables an embodiment of the presentdisclosure to identify individual cells of interest in a sample wellusing a microscope and a processor, then selectively activate a set ofactuators to manipulate a focus drive and a motorized stage upon which adeposition well plate is set. This well-plate solves the problem ofdistant movements among all of these stations (nanowells, receivingwells, wash wells). Existing solutions (e.g., CellCelector,micromanipulators) have separate stations that require distant travelfrom one location to the other. The well-plate shown in FIG. 5 furthercomprises two wash stations, three sampling wells for imaging, the wellshaving tapered walls to allow for picking close to their edges, an SBSfootprint, and covers to match the lid. The well-plate shown in FIG. 5has openings to accommodate 4×12-well PCR tube strips at a time. Thewell-plate is made of transparent material so that deposition of cellscan be imaged, or alternatively, filled with gel so that the plate canbe frozen in order to chill the wells.

After imaging of the sample wells, the captured images are analyzed andthe locations of individual cells of interest are automaticallydetected. In certain embodiments, the cell locations are confirmed by anoperator. The motorized stage and a focus drive are then automaticallytranslated such that a micromanipulator arm with attached capillaryneedle is oriented above a particular cell of interest. A verticalactuator corresponding to constrained movement in the z-direction of themicromanipulator arm is translated downward into the recovery well at orsufficiently near the individual cell of interest. A small quantity offluid containing the individual cell of interest is drawn by an actuatedmicropump into the capillary needle. Subsequently, the micromanipulatorarm is raised out of the sample well and the motorized stage and themicromanipulator arm are automatically translated such that a recoverywell is oriented beneath the micromanipulator arm. The capillary needlethen dispenses the fluid containing the individual cell of interest intothe recovery well.

Computing Environment

FIG. 7 shows an illustrative network environment 700 for use in themethods and systems for analysis of cytometry data corresponding toparticles of a sample, as described herein. In brief overview, referringnow to FIG. 7, a block diagram of an exemplary cloud computingenvironment 700 is shown and described. The cloud computing environment700 may include one or more resource providers 702 a, 702 b, 702 c(collectively, 702). Each resource provider 702 may include computingresources. In some implementations, computing resources may include anyhardware and/or software used to process data. For example, computingresources may include hardware and/or software capable of executingalgorithms, computer programs, and/or computer applications. In someimplementations, exemplary computing resources may include applicationservers and/or databases with storage and retrieval capabilities. Eachresource provider 702 may be connected to any other resource provider702 in the cloud computing environment 700. In some implementations, theresource providers 702 may be connected over a computer network 708.Each resource provider 702 may be connected to one or more computingdevice 704 a, 704 b, 704 c (collectively, 704), over the computernetwork 708.

The cloud computing environment 700 may include a resource manager 706.The resource manager 706 may be connected to the resource providers 702and the computing devices 704 over the computer network 708. In someimplementations, the resource manager 706 may facilitate the provisionof computing resources by one or more resource providers 702 to one ormore computing devices 704. The resource manager 706 may receive arequest for a computing resource from a particular computing device 704.The resource manager 706 may identify one or more resource providers 702capable of providing the computing resource requested by the computingdevice 704. The resource manager 706 may select a resource provider 702to provide the computing resource. The resource manager 706 mayfacilitate a connection between the resource provider 702 and aparticular computing device 704. In some implementations, the resourcemanager 706 may establish a connection between a particular resourceprovider 702 and a particular computing device 704. In someimplementations, the resource manager 706 may redirect a particularcomputing device 704 to a particular resource provider 702 with therequested computing resource.

FIG. 8 shows an example of a computing device 800 and a mobile computingdevice 850 that can be used in the methods and systems described in thisdisclosure. The computing device 800 is intended to represent variousforms of digital computers, such as laptops, desktops, workstations,personal digital assistants, servers, blade servers, mainframes, andother appropriate computers. The mobile computing device 850 is intendedto represent various forms of mobile devices, such as personal digitalassistants, cellular telephones, smart-phones, and

other similar computing devices. The components shown here, theirconnections and relationships, and their functions, are meant to beexamples only, and are not meant to be limiting.

The computing device 800 includes a processor 802, a memory 804, astorage device 806, a high-speed interface 808 connecting to the memory804 and multiple high-speed expansion ports 810, and a low-speedinterface 812 connecting to a low-speed expansion port 814 and thestorage device 806. Each of the processor 802, the memory 804, thestorage device 806, the high-speed interface 808, the high-speedexpansion ports 810, and the low-speed interface 812, are interconnectedusing various busses, and may be mounted on a common motherboard or inother manners as appropriate. The processor 802 can process instructionsfor execution within the computing device 800, including instructionsstored in the memory 804 or on the storage device 806 to displaygraphical information for a GUI on an external input/output device, suchas a display 816 coupled to the high-speed interface 808. In otherimplementations, multiple processors and/or multiple buses may be used,as appropriate, along with multiple memories and types of memory. Also,multiple computing devices may be connected, with each device providingportions of the necessary operations (e.g., as a server bank, a group ofblade servers, or a multi-processor system).

The memory 804 stores information within the computing device 800. Insome implementations, the memory 804 is a volatile memory unit or units.In some implementations, the memory 804 is a non-volatile memory unit orunits. The memory 804 may also be another form of computer-readablemedium, such as a magnetic or optical disk.

The storage device 806 is capable of providing mass storage for thecomputing device 800. In some implementations, the storage device 806may be or contain a computer-readable medium, such as a floppy diskdevice, a hard disk device, an optical disk device, or a tape device, aflash memory or other similar solid state memory device, or an array ofdevices, including devices in a storage area network or otherconfigurations. Instructions can be stored in an information carrier.The instructions, when executed by one or more processing devices (forexample, processor 802), perform one or more methods, such as thosedescribed above. The instructions can also be stored by one or morestorage devices such as computer- or machine-readable mediums (forexample, the memory 804, the storage device 806, or memory on theprocessor 802).

The high-speed interface 808 manages bandwidth-intensive operations forthe computing device 800, while the low-speed interface 812 manageslower bandwidth-intensive operations. Such allocation of functions is anexample only. In some implementations, the high-speed interface 808 iscoupled to the memory 804, the display 816 (e.g., through a graphicsprocessor or accelerator), and to the high-speed expansion ports 810,which may accept various expansion cards (not shown). In theimplementation, the low-speed interface 812 is coupled to the storagedevice 806 and the low-speed expansion port 814. The low-speed expansionport

814, which may include various communication ports (e.g., USB,Bluetooth®, Ethernet, wireless Ethernet) may be coupled to one or moreinput/output devices, such as a keyboard, a pointing device, a scanner,or a networking device such as a switch or router, e.g., through anetwork adapter.

The computing device 800 may be implemented in a number of differentforms, as shown in the figure. For example, it may be implemented as astandard server 820, or multiple times in a group of such servers. Inaddition, it may be implemented in a personal computer such as a laptopcomputer 822. It may also be implemented as part of a rack server system824. Alternatively, components from the computing device 800 may becombined with other components in a mobile device (not shown), such as amobile computing device 850. Each of such devices may contain one ormore of the computing device 800 and the mobile computing device 850,and an entire system may be made up of multiple computing devicescommunicating with each other.

The mobile computing device 850 includes a processor 852, a memory 864,an input/output device such as a display 854, a communication interface866, and a transceiver 868, among other components. The mobile computingdevice 850 may also be provided with a storage device, such as amicro-drive or other device, to provide additional storage. Each of theprocessor 852, the memory 864, the display 854, the communicationinterface 866, and the transceiver 868, are interconnected using variousbuses, and several of the components may be mounted on a commonmotherboard or in other manners as appropriate.

The processor 852 can execute instructions within the mobile computingdevice 850, including instructions stored in the memory 864. Theprocessor 852 may be implemented as a chipset of chips that includeseparate and multiple analog and digital processors. The processor 852may provide, for example, for coordination of the other components ofthe mobile computing device 850, such as control of user interfaces,applications run by the mobile computing device 850, and wirelesscommunication by the mobile computing device 850.

The processor 852 may communicate with a user through a controlinterface 858 and a display interface 856 coupled to the display 854.The display 854 may be, for example, a TFT (Thin-Film-Transistor LiquidCrystal Display) display or an OLED (Organic Light Emitting Diode)display, or other appropriate display technology. The display interface856 may comprise appropriate circuitry for driving the display 854 topresent graphical and other information to a user. The control interface858 may receive commands from a user and convert them for submission tothe processor 852. In addition, an external interface 862 may providecommunication with the processor 852, so as to enable near areacommunication of the mobile computing device 850 with other devices. Theexternal interface 862 may provide, for example, for wired communicationin some implementations, or for wireless communication in otherimplementations, and multiple interfaces may also be used.

The memory 864 stores information within the mobile computing device850. The memory 864 can be implemented as one or more of acomputer-readable medium or media, a volatile memory unit or units, or anon-volatile memory unit or units. An expansion memory 874 may also beprovided and connected to the mobile computing device 850 through anexpansion interface 872, which may include, for example, a SIMM (SingleIn Line Memory Module) card interface. The expansion memory 874 mayprovide extra storage space for the mobile computing device 850, or mayalso store applications or other information for the mobile computingdevice 850. Specifically, the expansion memory 874 may includeinstructions to carry out or supplement the processes described above,and may include secure information also. Thus, for example, theexpansion memory 874 may be provided as a security module for the mobilecomputing device 850, and may be programmed with instructions thatpermit secure use of the mobile computing device 850. In addition,secure applications may be provided via the SIMM cards, along withadditional information, such as placing identifying information on theSIMM card in a non-hackable manner.

The memory may include, for example, flash memory and/or NVRAM memory(non-volatile random access memory), as discussed below. In someimplementations, instructions are stored in an information carrier and,when executed by one or more processing devices (for example, processor852), perform one or more methods, such as those described above. Theinstructions can also be stored by one or more storage devices, such asone or more computer- or machine-readable mediums (for example, thememory 864, the expansion memory 874, or memory on the processor 852).In some implementations, the instructions can be received in apropagated signal, for example, over the transceiver 868 or the externalinterface 862.

The mobile computing device 850 may communicate wirelessly through thecommunication interface 866, which may include digital signal processingcircuitry where necessary. The communication interface 866 may providefor communications under various modes or protocols, such as GSM voicecalls (Global System for Mobile communications), SMS (Short MessageService), EMS (Enhanced Messaging Service), or MMS messaging (MultimediaMessaging Service), CDMA (code division multiple access), TDMA (timedivision multiple access), PDC (Personal Digital Cellular), WCDMA(Wideband Code Division Multiple Access), CDMA2000, or GPRS (GeneralPacket Radio Service), among others. Such communication may occur, forexample, through the transceiver 868 using a radio-frequency. Inaddition, short-range communication may occur, such as using aBluetooth®, Wi-Fi™, or other such transceiver (not shown). In addition,a GPS (Global Positioning System) receiver module 870 may provideadditional navigation- and location-related wireless data to the mobilecomputing device 850, which may be used as appropriate by applicationsrunning on the mobile computing device 850.

The mobile computing device 850 may also communicate audibly using anaudio codec 860, which may receive spoken information from a user andconvert it to usable digital information. The audio codec 860 maylikewise generate audible sound for a user, such as through a speaker,e.g., in a handset of the mobile computing device 850. Such sound mayinclude sound from voice telephone calls, may include recorded sound(e.g., voice messages, music files, etc.) and may also include soundgenerated by applications operating on the mobile computing device 850.

The mobile computing device 850 may be implemented in a number ofdifferent forms, as shown in the figure. For example, it may beimplemented as a cellular telephone 880. It may also be implemented aspart of a smart-phone 882, personal digital assistant, or other similarmobile device.

Various implementations of the systems and techniques described here canbe realized in digital electronic circuitry, integrated circuitry,specially designed ASICs (application specific integrated circuits),computer hardware, firmware, software, and/or combinations thereof.These various implementations can include implementation in one or morecomputer programs that are executable and/or interpretable on aprogrammable system including at least one programmable processor, whichmay be special or general purpose, coupled to receive data andinstructions from, and to transmit data and instructions to, a storagesystem, at least one input device, and at least one output device.

These computer programs (also known as programs, software, softwareapplications or code) include machine instructions for a programmableprocessor, and can be implemented in a high-level procedural and/orobject-oriented programming language, and/or in assembly/machinelanguage. As used herein, the terms machine-readable medium andcomputer-readable medium refer to any computer program product,apparatus and/or device (e.g., magnetic discs, optical disks, memory,Programmable Logic Devices (PLDs)) used to provide machine instructionsand/or data to a programmable processor, including a machine-readablemedium that receives machine instructions as a machine-readable signal.The term machine-readable signal refers to any signal used to providemachine instructions and/or data to a programmable processor.

To provide for interaction with a user, the systems and techniquesdescribed here can be implemented on a computer having a display device(e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor)for displaying information to the user and a keyboard and a pointingdevice (e.g., a mouse or a trackball) by which the user can provideinput to the computer. Other kinds of devices can be used to provide forinteraction with a user as well; for example, feedback provided to theuser can be any form of sensory feedback (e.g., visual feedback,auditory feedback, or tactile feedback); and input from the user can bereceived in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in acomputing system that includes a back end component (e.g., as a dataserver), or that includes a middleware component (e.g., an applicationserver), or that includes a front end component (e.g., a client computerhaving a graphical user interface or a Web browser through which a usercan interact with an implementation of the systems and techniquesdescribed here), or any combination of such back end, middleware, orfront end components. The components of the system can be interconnectedby any form or medium of digital data communication (e.g., acommunication network). Examples of communication networks include alocal area network (LAN), a wide area network (WAN), and the Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

Nanobox for Performing High Quality Multicolor Slide Cytometry

In certain embodiments, the processor includes a fully integrated module(“Nanobox”) for performing high quality multicolor slide cytometry.Nanobox has several components that automate image calibration, nanowellgrid identification, cell identification, data extraction, spectralspillover compensation, and in silico F-minus one generation forautomatic calling of positive signal. This process traditionallyrequires measuring an independent sample to collect F-minus one data forcompensation, which can be accomplished with beads stained with allfluorophores used (minus one) in different combinations. This is atedious process. The best results are achieved when these samples usecells instead of beads since they are more similar to the cells tested.The limitation is that the cells can be extremely limited in number fromtissue. The in silico process described herein solves this problem byenabling compensation directly from the tested cells (no calibrationsample required).

In certain embodiments, all steps can be accomplished with about 5minutes or less of hands-on user input and use a parallel processingframework for rapid, distributed data analysis. The module can be usedboth in stand-alone mode (e.g., for processing a set of specimen imagesacquired previously), and real-time mode (e.g., for processing specimenimages as they are being acquired). FIG. 9A is a flow chart 900depicting steps performed by an exemplary nanobox module, according toan illustrative embodiment. Step 902 is automated image calibration,step 904 is nanowell grid identification, step 906 is cellidentification, step 908 is data extraction, step 910 is spectralspillover compensation, and step 912 is in silico F-minus onegeneration. FIG. 9B depicts further detail involved in the insilicoF-minus one generation step 912.

Image Calibration (Reference 902)

Nanobox automatically extracts from the metadata of the image files theidentity of the microscope used to acquire the data, the date the datawas acquired, and all relevant information about the spectral channelsused in the experiment (or in real-time mode, obtains relevantinformation directly from other modules). The metadata is used toautomatically select from a library of calibration frames, theappropriate dark, flat field, and illumination calibration frames foreach spectral image for that microscope on the date the data wascollected. The calibration library is populated by running definedcalibration programs on the microscope monthly. The final calibratedimage is created through the equation:I _(c)=((I _(r) −DF)./FF)./IFwhere: I_(c)=Calibrated image, I_(r)=raw image, DF=dark frame, FF=flatfield frame, IF=illumination frame and ./ denotes element-wise division.

Dark Frame—Dark calibration frames are created by collecting a series ofimages with no excitation light using a combinatorial combination ofexposure times and camera gain settings in the typical range forcytometry. All images acquired with the same combination of exposuretime and gain are averaged to create the dark frame for those settings.Nanobox selects the dark frame with the closest combination of exposuretime and gain used for each spectral channel.

Flat Field Frame—The flat field calibration frames are created bycollecting a series of images of unstructured transmitted light passingthrough the dichroic and each emission filter. The images for eachfilter are averaged. The dark calibration frame acquired with the sameexposure and gain settings is subtracted from the flat field image andthen the image is normalized by the mean pixel intensity of all pixelsin the image to acquire the flat field calibration frame for eachemission filter. Nanobox selects the correct flat field frame to applyby determining the emission filter used for each spectral channel in thedataset.

Illumination Field Frame Illumination calibration frames are created byacquiring series of images of a set of uniformly fluorescent slidesusing each excitation channel. Images within each image series thatcontain unique anomalies in the fluorescent field are automaticallydiscarded. The remaining images are averaged. The appropriate dark frameis subtracted from the averaged image followed by division by theappropriate flat field frame. Finally, the image is normalized by themean pixel intensity to acquire the illumination calibration frame foreach excitation channel. Nanobox selects the correct illumination fieldframe to apply by determining the excitation light used for eachspectral channel in the dataset.

Alternatively to constructing a library of images for each set ofmicroscope settings, dark frame calibration may be performed at thestart of each acquisition of specimen data. This procedure providesprotection against extraneous changes (e.g., due to shifts in theoptical train caused by vibration or accidentally by a user) happeningbetween runs, and increases accuracy of the method. Flat field andillumination field frames are then adjusted accordingly.

Nanowell Grid Identification (Reference 904)

Nanobox employs a fully-automated algorithm to identify the location ofnanowells in the spectral image series of each array location. Thealgorithm initially uses a Radon transformation of the transmitted lightimage to identify the rotation of the nanowell grid in the image. Thetransmitted light image is then rotated the calculated amount. 1-Dprojections of the rotated image onto the x- and y-axes are analyzed forthe expected frequency of well edges based on the well size and wellspacing, identifying starting and stopping points coordinates for wellsin the image. Often, nanowell arrays are designed where the inter-wellspacing is equivalent to the well size (e.g., 50 μm wells with 50 μmspacing), making it impossible to determine from the 1D traces whichsignals represent the start or end of wells. This is determined byNanobox by calculating similar 1D projections of a Sobel transformationof the rotated image, which turns the image into a binary image whereonly edges have a value of 1. Projections of the well areas will havehigher values in these 1D traces than the inter-well spaces due to thepresence of the edges parallel to projection axis. With thisinformation, Nanobox defines the location of the x- and y-grid of thenanowells in the image. Finally, the algorithm uses simple trigonometryto transform the coordinates of each well to designate the same area inthe original image.

Cell Identification and Data Extraction (References 906 and 908)

In certain embodiments, nanobox uses a segmentation thresholdingalgorithm for identifying weak signal above background that was designedto be agnostic to the number of cells present in the image. Thealgorithm applies a sliding threshold to each spectral channeldesignated by the user to be used to identify cell locations. For eachthreshold, the number of single pixel objects in the binary imagedefined by the threshold are calculated. Plotting the threshold valueversus the number of single pixel objects creates an L shaped curvewhere the number of single pixel objects changes little as the thresholdis decreased until an inflection point is reached where the number ofsingle pixel events increases rapidly with decreases in threshold. Asimple geometric analysis of this plot (e.g., finding the intersectionof two chord lines) is used to determine the threshold at the inflectionpoint, yielding the lowest threshold possible before a large amount ofbackground signal would be called positive. By focusing on single pixelobjects, the algorithm bases the threshold solely on the distribution ofbackground pixels, because positive signals from cells rarely appear assingle pixel events. This makes the algorithm far more robust tovariances in the number of cells in the image compared to otherauto-thresholding algorithms that use statistical measures tocharacterize total pixel distributions.

Additionally, Nanobox is able to further split the segmented areascomprising two or more adjacent cells (e.g., based on the invertedwatershed method seeded by the local maxima in pixel intensities).

After segmentation, Nanobox applies various astronomical aperturephotometry concepts in new ways to measure the intensity of fluorescentsignal in each spectral channel. To maximize the signal to noise ratio(SNR) of the signal extracted from each cell, an aperture of 5-8 pixelsis set within the defined cell area to minimize dilution of the signalfrom the decrease of signal near the periphery of the cell. The aperturefor each cell is defined by rank ordering the intensity of thefluorescent signal in the spectral channel used to define the locationof the cell and selecting the brightest 5-8 pixels. The signal for thosepixels in all other spectral channels is then extracted and exported forfurther analysis. If multiple segmentation channels are used, Nanoboxcreates a unique “cell object” for each segmentation channel, andanalyses it separately from the rest.

Spectral Spillover Compensation (Reference 910)

In order to quantitate the number of fluorescent molecules detected in agiven spectral channel on a cell, spillover of signal from fluorophoresin neighboring spectral channels into the current channel must be takeninto account. During the initial setup, Nanobox loads images of beadsstained singly with each fluorophore used to stain the cells. Nanoboxautomatically locates beads within the images (e.g., using the samesegmentation algorithm as for cells) and extracts the average spilloverspectral properties of each fluorophore. Using well establishedtechniques from flow cytometry, Nanobox automatically uses thisinformation to determine the true amount of each fluorophore present oneach cell by multiplying the raw intensities extracted from eachspectral channel by the inverse spillover matrix generated from thespillover beads. This data is exported as final compensated fluorophorecounts for each cell.

In Silico FMO (FIG. 9A, Reference 912, and FIG. 9B)

Fluorescence-based cytometry is a widely used tool for the analysis ofprotein and more recently RNA expression of large number of singlecells. Recent advances in optics and dye chemistry have enabled thesimultaneous measurement of 10-30 different fluorescent molecules oneach cell. However, this depth of data acquisition ensures significantspillover between the spectral channels due to the broad excitation andemission peaks of most fluorescent dyes. To calculate the amount of eachfluorophore on a cell, the spillover signal in the fluorescent channeldesignated for the fluorophore is removed through an in silico processcalled compensation. Although required, compensation inevitably addsconsiderable error to the resulting measure, manifesting in a wideningof the data distribution and often adding a skew to the distribution,the result of which makes it challenging to determine where a positivethreshold should be set in a robust and reproducible manner.

It is possible to implement “F-minus one” (FMO) controls. in whichseparate portions of the same sample are stained with an antibody panelthat contains all the antibodies but one. The distribution of the signalof the removed fluorophore is used to define the positive threshold forthe missing dye as it is known that all cells are negative in thecontrol. Although critical for accurate positive cell calling, FMOs arevery burdensome to perform especially in high content cytometry becausean FMO control is required for each channel. For staining a singlesample of 100,000 cells with 12 antibodies for ˜$18 in reagents, theFMOs require over a million cells and $190 in reagents and significantlymore FTE time. When hundreds of samples are contemplated, FMOs become aserious drain of resources. Even more challenging, many types ofclinical samples simply do not have enough cells to perform thecontrols, complicating analysis of these precious samples with thispowerful technology.

Beyond cost and cell requirements, the classic FMO approach has anotherweakness in that it sets global thresholds based primarily on cells withthe highest amounts of error (e.g., edges of the negative signaldistribution) caused by the highest amounts of spillover signal frombright staining in another channel. This weakness is a result of usingan average spillover value, making it impossible to create a predictednegative distribution without considering all the cells. This approachhowever has significant consequences for the sensitivity of detectingfluorescence on cells with little spillover, where the error will bemuch lower than cells with high levels of spillover. Segregation of thepopulation into similar cells before applying FMOs is also a limitedtechnique.

Nanobox also employs an algorithm for determining the probability thatthe compensated fluorescent signal for a given cell is higher than thebackground, taking into account the added error from spillovercompensation. While extracting the basic spillover data from thesingly-stained beads, Nanobox also builds n×(n−1) 3-dimensionalprobability distributions of the amount of signal measured in eachspectral channel associated with a given amount of each fluorophore.Once Nanobox has determined the amount of each fluorophore on a givencell, it creates n “F-minus one” (FMO) replicas of the cell in which thevalue of one of the fluorophores is set to zero in each FMO. Forpopulation level analysis mode, the expected amount of measured signalin the channel measuring the fluorophore set to zero is determined byrandomly sampling the 3D probability distributions of the otherfluorophores' spillover signal into that channel using the calculatedamount of each fluorophore on the cell. This signal is added to a randomsampling of the background pixel signal distribution in the channel,creating the “pseudo-raw” FMO data. These values are then subjected tothe same compensation calculation used on the real data, yielding theexpected signal distribution of cells containing no fluorophores for thechannel, given the population distribution of all the otherfluorophores. This negative distribution can then be used to calculatethe probability that the signal associated with any cell issignificantly higher than the expected population background. Thisapproach can also be performed on a per cell basis. In this approach,each cell is used to make 10,000 FMO replicates, each of which is usedto create raw FMO values by randomly sampling the 3D spillover signaldistributions. These FMOs are then used to create a unique negativesignal distribution for each cell based on the amount of eachfluorophore on that cell. This approach is useful when analyzing smallnumbers of cells where there are not enough cells to create populationlevel FMOs. It also increases the sensitivity to low amounts of signalby considering only the amount of spillover error appropriate for thefluorophores present on each particular cell instead of the averageamount of error for the population which is the standard in the fieldtoday.

FIG. 9B shows an exemplary flow chart 950 for in silico FMO generation.Prior to execution of the in silico FMO generation procedure, certaindata is acquired. In certain embodiments, acquired data includes:n-spectral images of compensation beads stained individually with eachfluorescent molecule acquired with same microscope settings as cells andn-spectral images of cells stained with all fluorescent molecules. Step952 is identification of the location of the beads, step 954 isextraction of signal intensity of each pixel in all spectral channelsfor each bead, step 956 is creation of a 3D probability matricesrelating intensity of signal in the spectral channel assigned tofluorophore to the signal in each of the other channels (See FIG. 11below for detail), step 958 is identification of the location of cellsin images, step 960 is extraction of signal in all channels for eachcell, step 962 is extraction of signal from similar sized areas notcontaining cells (background signal), step 964 is determination ofamount of each fluorophore on cells through using average spillovervalues extracted from probability matrices and standard linearcompensation, step 966 is creation of n-replicas of each cell'scompensated fluorophore content (in each replica, one fluorophorecontent is zeroed by replacing the value with a sample taken from thebackground signal distribution), step 968 is generation of sample 3Dspectral probability matrices to calculate expected distribution of rawfluorescent signal in each channel based on concentration of eachfluorophores, step 970 is compensation of reconstructed pseudo-rawfluorescent values to create distribution of calculated signal on cellsdefined as having no real fluorophores present “Population-level insilico FMOs”, and step 972 is resampling of 10,000-1000,000 times foreach cell to generate expected negative cell distribution for eachindividual cell “Single cell in silico FMOs”. FIG. 10 contains theprobability that a given amount of fluorophore will emit a given amountof light detected in a second spectral channel. N−1 matrices aregenerated for each fluorophore.

FIG. 11 shows a flow chart 1100 of generating 3D spectral probabilitymatrices. The data is derived from spectral intensity of pixelsextracted from images of beads labeled with single fluorophores.Generating a matrix relating to the signal in spectral channel B toamount of fluorophore A is performed via using data from beads labelswith fluorophore A in accordance with the steps 1102-1110 provided inFIG. 11. Step 1102 is determination of average amount of light emittedin channel B by fluorophore A through linear regression, step 1104 isnormalization of B signal to 0 by subtracting product of fluorophore Aconcentration and slope of the linear regression, step 1106 is binningof data into overlapping bins based on fluorophore A concentration, step1108 is creation of 2D probability distribution of B signal for eachbin, normalized to 1, and step 1110 is combination of 2D distributionsinto one matrix to create 3D spectral probability matrix.

In order to enable high content cytometry of small cell samples, analgorithm for generating FMO controls was generated in silico using thedata already collected for the initial compensation of the spilloversignal. The algorithm works by generating what a 3D spectral probabilitymatrices from typical compensation bead data, as described above. Eachspectral matrix contains the probability that a given amount of afluorophore will produce any given amount of spillover signal in adifferent spectral channel. When expressed graphically, the x-axis isconcentration of a fluorophore, the y-axis is signal in a particularspectral channel and the z axis is the probability that any twocombinations will occur. A unique matrix is generated for eachcombination of fluorophore and spectral channel. To generate in silicoFMOs, the compensated cell data is replicated n-times for an n-channelexperiment. In each replicate dataset, the concentration of a differentfluorophore is set to zero. The spectral matrices are then sampled torecreate a “pseudo-raw” intensity dataset which predicts thedistribution of raw signal that would be predicted to occur in thechannel if no actual fluorophore was present. The dataset is thenrecompensated to yield the signal distribution of the negativepopulation. By sampling the probability distributions of spectralspillover instead of a single average value, errors added to thenegative cell distribution including distribution skewing that is lostwith the classical approach are able to be predicted. The predictednegative cell distribution can then be used to automatically set apositive threshold or give a p-value for the probability that any givencell has positive fluorescent signal, replicating the output of classicFMO controls. This is referred to as population level FMO generation.

In certain embodiments, the algorithm can create unique negativedistributions for each cell in the dataset based on the specificconcentration of fluorophores on each cell. The fluorophore content ofeach cell is used to iteratively sample the spectral matrices, creatinga dataset of all the possible fluorescent signal intensities that wouldbe observed given the amount of fluorophores on any cell, minus one.This data is then processed like the population level data to createnegative signal distributions for each cell. This approach has two majoradvantages—increasing sensitivity to low amounts of signal on cells withlittle spillover and more robust FMO generation for samples with smallnumbers of cells. This is referred to as individual FMO generation.

The algorithm generates robust signal distributions of negative cellsthat takes into account all errors introduced into high contentcytometric data during compensation. In certain embodiments, thealgorithm is used to automatically call cells positive based onpopulation level thresholds. In certain embodiments, the algorithmgenerates a predicted distribution for each individual cell, increasingsensitivity to low signal levels on cells with little spillover error.The algorithm can eliminate a major drain on resources in high contentcytometric analysis as well as enable the use of the technology on smallcell samples. The algorithm is applicable to either flow or slide basedcytometry and likely has utility in other settings with high levels ofmultiplexing.

In certain embodiments, the algorithm enables robust automated callingof positive fluorescent signal in the context of significant crosstalkbetween fluorescent channels. In certain embodiments, the algorithmgenerates critical for high content cytometry, both flow cytometry andslide-based cytometry. In certain embodiments, the algorithm providesvast improvement over current approach in flow which use F-minus onecontrols—large saving in time, reagents and precious sample. In certainembodiments, the algorithm enables high content cytometry for small cellclinical samples that do not have enough cells to perform FMO controls.

EXAMPLE

The present example provides improvements compared to current flowsystems. These improvements are particularly useful, although notlimited, in the field of immunohistochemistry of tissue sections andhighly multiplexed microarrays, where imaging of samples with limitedlifespan and analyzing results in real-time is critical.

The features described below improved the image quality compared toconventional systems. For example, most single sensor imagespectrometers achieve separation of each subimage by having each beamhit the lens at different angles. However, this can cause significantimage aberrations which are significantly amplified in wide fieldsettings. To overcome this, each beam is instead focused on a mirrorarray facing the final imaging lens. The angles of the mirrors in thearray reflect the each beam directly toward the final lens, essentiallyrecreating a new image on the mirror array that has each beam focused ina separate section.

The approach described herein focuses on each spectral channel. Tocorrect the longitudinal chromatic aberration caused by filtering aconverging beam and the other optical components, the individualreflecting mirrors that aim each beam at the central mirror array can beindependently adjustable along the primary optical axis, enablingshortening or lengthening the focal point of each spectral colorindependently, allowing perfect correction of longitudinal chromaticaberration, critical for cytometric analysis of pinpoints of light.

Data Points on Imaging Speed:

The described improvements below provided an imaging speed of 55 mm²/minin 12 fluorophores or channels, which can be extrapolated to 45 cm²/minfor 16 fluorophores or channels of data. In certain embodiments, thespeed reaches 300 mm²/min for imaging both 12 and 16 fluorophores orchannels and includes 8 channels using a microscope. FIG. 13 shows a CADdrawing of a microscope that would perform imaging speeds at about 300mm²/min.

Referring to FIG. 13, (1) is a LLG collimator, (2) is a field stop iris,(3) is a UV LED collimator, (4) is a UV dichroic, (5) is 1650 μm² Squareslit field stop iris, (6) is an Objective, (7) is a Quad dichroic, (8)is a Turning mirror, (9) is a Focal lens, (10) is an Image plane, (11)is a 1650 μm² Slit, (12) is a Converging lens, (13) is a Mirror, (14) isa Band 1-3 dichroic, (15) is a Band 1 dichroic—back, (16) is a Rightturning mirror and Band 1 filter behind, (17) is a Band 4-5 dichroic,(18) is a Mirror, (19) is a Band 2 dichroic—forward, (20) is a Rightturning mirror and Band 2 filter in front, (21) is a Band 7 DichroicBack (behind 7 filter), (22) is a Band 4 dichroic—back, (23) is a Rightturning mirror and Band 4 filter behind, (24) is a Band 3 filter, (25)is a Turning mirror forward (band 8 filter), (26) is a Band 2dichroic—forward, (27) is a Right turning mirror and Band 5 filter infront, (28) is an Image mirror array, (29) is a Band 6 filter, (30) are3 relay lens (front, plane, back), (31) are 2 relay lens (front, back),(32) are 3 relay lens (front, plane, back), (34) is a Final focal lens,(35) are 2 focusing mirrors in front and behind, (36) are 3 focusingmirrors, and (37) is a CMOS.

Comparators:

Commercial Zeiss microscope designed for similar imaging providesimaging speeds of about 7 mm²/min for 16 fluorophores or channels.

High-end Genepix slide scanners with the same resolution as the systemdescribed herein is approximately 100 mm²/min for 1 fluorophore orchannel, which can be extrapolated to 8 mm²/min for 12 fluorophores orchannels. Calculations of imaging speeds are shown in Table 1.

In Table 1, the Present system, as described herein, estimates for 12channels on the present system (not including the improvements describedbelow with respect to FIG. 13) are based on an imaging run that producedthe data in FIGS. 14A and 14B, discussed in more detail below. The 16channel times were extrapolated based on time/channel of the 12 channelrun. The Further updated system is a constructive embodiment, asdepicted and described in more detail below with respect to FIG. 13,where time estimates were calculated theoretically based on the timerequired to image the same number of channels on the Present system. Theslide scanner (Genepix 4400—Molecular Devices) timing is derived fromtime stamps of 4 channel scans of similarly sized glass slides at thesame spatial resolution. Scanning time is directly scalable to channelsas it performs the same process for each channel. The commercial scope(Zeiss AxioObserverZ1) times are based on an average of imaging runsperformed.

TABLE 1 Field of Number Number Number Time per Total Total view, of ofof position, area, time, Speed, System mm columns rows channels ms mm²min mm²/min Commercial 0.8 24 72 16 1105.9 150 7 microscope Commercial12 1518 180 8 slide scanner Present system, 1.25 15 46 16 2135 1078.124.6 44 hardware-triggered Present system, 1.25 15 46 12 1735 1078.1 2054 hardware-triggered Further updated 1.65 11 35 23 650 1048.2 4.2 250system, hardware- triggered (all data estimated) Time to move 150 thestage, ms Time to switch the 55 filter wheel position, ms Number offilter 8 wheel positions Average exposure 100 time, ms Number of 5 lightsourcesImprovements to System:

An altered optical train is implemented by adding a demagnification lensto optimize resolution for cytometry and to increase imaging speed.

Moreover, the addition of the demagnification lens (e.g., a 0.8× orless, 0.7× or less (e.g., 0.63×), 0.6× or less, 0.5× or less, or withina range from 0.4× to 0.8×, or from 0.5× to 0.7×), to the optical trainachieves the minimum resolution needed for robust cytometry analysis.Demagnification enables imaging more surface area/image leading to3-fold improvement in imaging speed while maintaining the high NA of the10× objective.

Software development enables use of a higher powered light source, whichfacilitated lower exposure times for same sensitivity. A CMOS camerareplaces an EMCCD to increase number of pixels by 16-fold, which enabledimaging at a 1.6-fold larger field of view and reduced imaging time byhalf.

Hardware triggering of all components, for example, controlled by TTLtriggering using a commercial microcontroller (Arduino) with localmemory, is implemented to achieve imaging speeds currently not providedby standard systems. As a result, the computer does not need tocommunicate with any hardware during an imaging run, which reducesimaging time by 40%.

To enable efficient communication between various components of theimaging system, the components are connected to a microcontrollercapable of sending and receiving digital (e.g., TTL) signals withminimum (e.g., on the order of μs) delays and without interaction with acomputer (FIG. 12). This approach leads to a considerable speed-up ofacquisition of multi-spectral data across multiple spatial positions,since the delays normally introduced by slow serial connections, as wellas systems-level inefficiencies of the software running on the computer,are factored out.

Acquisition time is defined only by movements of mechanical parts of theimaging system, triggering of the light source(s), as well as intervalsof exposure of the detector to light and/or readout of signal from thelatter. An efficient triggering scheme comprises the following steps, asdepicted in FIG. 12:

-   -   0. Computer transmits spatial positions to the memory of Stage,        and filter positions to the memory of Filter Wheel        (additionally, Computer pre-arranges filter positions to        minimize Filter Wheel movements). Other fixed parameters (e.g.,        EM gain for Detector) can also be pre-populated as appropriate.        This transfer can be done over a slow (e.g., serial) link.    -   1. Computer transmits other parameters of acquisition (e.g., the        number of positions and spectral channels to be acquired, as        well as Light Source(s), exposure times and Filter Wheel        movements to be set for each channel) to Microcontroller. This        transfer can also be done over a slow (e.g., serial) link. This        step is not typical for imaging microscopes. The information is        transmitted to the system for processing all at once rather than        typical serial handshaking serial processing with the computer        and hardware. It allows the microcontroller to perform the        acquisition without constantly checking with the computer.    -   2. Microcontroller starts a cycle of acquisition by signaling        Stage to move to the next position stored in its memory in step        0.    -   3. Stage moves to the stored position and signals        Microcontroller upon completion of the move.    -   4. Microcontroller signals Filter Wheel to move to the next        position stored in its memory in step 0.    -   5. Filter Wheel moves to the stored position and signals        Microcontroller upon completion of the move. To save time, steps        4 and 5 for the first Filter Wheel position may be performed        between steps 2 and 3, since Stage movements typically take        longer than Filter Wheel movements (e.g., 150 ms vs 50 ms).    -   6. Microcontroller signals Light Source(s) to turn them on. The        time spent on ramping up light intensity to a full level is        typically negligible (e.g., 10 μs<<1 ms) for solid state        sources, in which case no signal needs to get sent back from        Light Source(s), but rather a small delay (e.g., 1 ms) can be        introduced to allow for this step to complete.    -   7. Microcontroller signals Detector to start integration of        light.    -   8. Upon completion of exposure time, Microcontroller signals        Light Source(s) to turn them off.    -   9. Microcontroller signals Detector to stop its integration.        Detector automatically transfers accumulated image to a frame        grabber on the computer. Depending on the model of Detector,        this transfer can be done simultaneously with step 7.    -   10. Steps 4-9 are repeated for the rest of spectral channels in        the current spatial position.    -   11. The next cycle of acquisition begins, starting with step 2.

A micromanipulator is integrated into imaging hardware and software sothat it is not necessary to transfer cells to another machine andrecalibrate.

Real-time processing of image data is implemented. By contrast,processing of data via other methods can take a couple hours, whichdramatically slows time for picking cells.

Moreover, the imaging beam can be reconfigured to enable imaging of allchannels utilizing the same excitation band simultaneously (e.g., incontrast to serially (e.g., typically 4-6 channels per excitation)).This process is often called imaging spectrometry, which includes aseries of dichroic mirrors to split image into separate image beamsbased on different wavelengths and then all beams are aimed at differentsections of the same CMOS camera, taking 5 or 6 non-overlapping imagesat the same time. This cuts the number of images required for 16 colorsfrom 16 to 4, leading to a 3-fold increase in imaging speed.

In certain embodiments, a second light source is added to bring thenumber of available spectral channels to 23 channels, higher than anycommercial flow cytometer. This feature emphasizes that the scaling ofthe machine for new channels is far more efficient than flow cytometrydesigns because the same detection channels are used for all excitationwavelengths. In contrast, flow machines must duplicate these processesbecause the cells are moving past the detectors.

Moreover, a square excitation field stop can be added, which, in certainembodiments, is critical for tissue imaging applications by limitingillumination to only square being imaged, thereby preventingphotobleaching.

The following is an illustrative list of steps performed in a triggeringexperiment, together with their timings:

-   Position 0 started-   Wheel moved. 55 msec-   Stage moved. 106 msec-   Channel 0 acquired. 207 msec-   Wheel moved. 54 msec-   Channel 1 acquired. 157 msec-   Channel 2 acquired. 101 msec-   Channel 3 acquired. 101 msec-   Wheel moved. 54 msec-   Channel 4 acquired. 256 msec-   Channel 5 acquired. 201 msec

Wheel moved. 56 msec

-   Channel 6 acquired. 157 msec-   Channel 7 acquired. 101 msec-   Channel 8 acquired. 101 msec-   Wheel moved. 100 msec-   Channel 9 acquired. 201 msec-   Wheel moved. 56 msec-   Channel 10 acquired. 157 msec-   Wheel moved. 54 msec-   Channel 11 acquired. 156 msec-   Position 0 acquired-   Total time: 1896 msec    Innovation in Imaging Optics to Enable Precise Image Spectrometry of    Wide Field, High Aperture Images

To enable precise imaging spectrometry of wide-field high apertureimages, the imaging beam is separated as a converging beam instead ofcollimated beam. In contrast to a converging beam, a collimated beam ofa wide field image becomes far too wide for efficient opticalseparation. A converging beam enables fast, high-throughput imaging andanalysis.

FIG. 14A is a scatter plot depicting beads that were labeledindividually with 11 different fluorophores and imaged. The intensity offluorescence after compensation for all beads is plotted. Each beaddisplays signal in only a single channel (all beads align along the axesof each plot). Polystyrene beads coated with anti-mouse antibody wereused to bind mouse antibodies labeled with BV785, AF488, PerCP,PerCP710, PE, PECy5, PEAF700, PECy7, AF647, and APCCy7. Calcein violetwas used to stain human PBMC. Each individually stained bead or cell wasloaded into a well of a 96 well plate and all wells were imaged in 11spectral channels using the Present system. The intensity of each beadin all spectral channels was extracted. The average spillover signalfrom each fluorophore in each spectral channel was calculated. Thespectral data was used to compensate the signal emitted by each bead.The compensated data for all the beads and cells was then pooled and 2Dscatter plots comparing the compensated signal each channel compared toall other channels is displayed.

FIG. 14B shows cells that were labeled with fluorescent markers andimaged on the Present system at a speed of 60 mm2/min for 12 channels.Hierarchical gating of the data after data extraction and compensationis displayed for some common cell phenotypes.

Human peripheral blood mononuclear cells were labeled with BV785-HLADR,AF488-CCR7, PerCP-CD3, PerCP710-CD16, PE-CD19, PECy5-CD56, PEAF700-CD4,PECy7-CD45RA, AF647-CD45, and APCCy7-CD8 antibodies. The cells wereloaded into an array of 50 um³ wells molded in PDMS on a glass slide.The array was imaged using the Present system at a speed of 60 mm²/minfor 12 channels (11 fluorescent+transmitted light). The intensity ofsignal in each spectral channel was extracted from each image. The datawas compensated using spectral spillover values measured using singlystained beads. The resulting data was logicle transformed andhierarchically gated using 2D scatter plots to identify common cellphenotypes.

Data was acquired on a microscope of the Present system consisting of aframe (ASI), filter wheel (ASI), TI2500 stage (ASI), 125D tube lens(ASI), TI2500 automated stage (ASI), ImagEM EMCCD camera (Hamamatsu),dichroic cube, SpectraX light engine (Lumencor), a LED light fortransmitted light and a 10×/0.3NA Plan apochromatic objective (Olympus).All filters and the dichroic mirror were acquired from Semrock exceptthe 710/20 and 810/80 filters (Omega). Each channel was acquired for 100msec at a gain of 10 and excitation power of 100%. The stage, filterwheel, light source and camera were all controlled through TTLtriggering using an Arduino board. The filters were used in thefollowing combinations to create the indicated spectral channel:

Spectral channel Exc. Em. Calcein Violet 380/14 440/40 BV785 380/14810/80 AF488 485/25 525/39 PerCP 485/25 684/24 PerCP710 485/25 710/20 PE560/25 607/36 PECy5 560/25 684/24 PEAF700 560/25 710/20 PECy7 560/25810/80 AF647 648/20 684/24 APCCy7 648/20 810/80FIG. 15 shows plots of fluorescent intensities of cells extracted from aset of images by nanobox and in silico FMO. Human PBMC were stained withCD4-PEAF700, CD16-PerCP710, CD3-PerCP, CD45RO-PECy5, CD8-APCCy7,CD45RA-PECy7, CCR7-PE, CD25-BB515 antibodies and calcein violet livestain. Cells were loaded into a nanowell array and imaged in 9 spectralchannels. Fluorescent intensities of each cell were extracted from theimages by nanobox and in silico FMO were generated from the data. The insilico FMO were generated for each channel except CD3, which was used toidentify cell locations (and could not be used for FMO generation), andare plotted with their corresponding plots of the fully stained cells.

EQUIVALENTS

While the invention has been particularly shown and described withreference to specific preferred embodiments, it should be understood bythose skilled in the art that various changes in form and detail may bemade therein without departing from the spirit and scope of theinvention as defined by the appended claims.

What is claimed is:
 1. A system for automated identification andrecovery of individual cells of interest, the system comprising: amicroscope comprising a light source, an optical train, and a detectorcapable of imaging a deposition-well plate positioned on a motorizedstage; a motorized stage and a set of actuators configured to translatethe stage in a first direction and a second direction in a horizontalplane; a motorized focus drive to translate an optical objective of themicroscope in a vertical direction; a micromanipulator arm comprising anactuator configured for constrained movement of the micromanipulator armin the vertical direction to calibrate a location of a capillary needlewithin an imaging field of view of the detector, wherein the capillaryneedle is removably fastened/fastenable to the micromanipulator arm andoriented in the vertical direction; one or more deposition-well platescomprising one or more sample wells and/or one or more recovery wells,wherein the deposition-well plates are removably attached/attachable tothe motorized stage; and a processor of a computing device, wherein theprocessor is configured to send a series of control signals to cause:(i) the microscope to capture an image of a first sample well, whereinthe processor is further configured to analyze the image to identify alocation of an individual cell of interest within the first sample well;(ii) the set of actuators to translate the motorized stage according tothe identified location of the individual cell of interest within thefirst sample well, such that the capillary needle is oriented above theindividual cell of interest; (iii) the actuator to translate, in thevertical direction, the micromanipulator arm to orient a tip of thecapillary needle in the first sample well at or sufficiently near theindividual cell of interest; (iv) introduction of a volume of fluidcomprising the individual cell of interest into the capillary needle;(v) the actuator to translate, in the vertical direction, themicromanipulator arm such that the capillary needle containing thevolume of fluid comprising the individual cell of interest is raised outof the first sample well; (vi) the set of actuators to translate themotorized stage, such that the capillary needle containing the volume offluid comprising the individual cell of interest is oriented above thefirst recovery well; (vii) the actuator to translate, in the verticaldirection, the micromanipulator arm such that the capillary needlecontaining the volume of fluid comprising the individual cell ofinterest is lowered into the first recovery well; and (viii) a releaseof the volume of fluid thereby depositing the individual cell ofinterest into the first recovery well; and wherein the processor isconfigured to perform a multi-point calibration of a surface of thedeposition-well plate to correct spatial variations in three-dimensionalspace, thereby providing a coordinate system enabling the microscopestage and the motorized focus drive to be automatically translated bythe processor, wherein the multi-point calibration comprises positioningthe motorized stage at positions corresponding to one or more locationsof an imaging region of the deposition-well plate; identifyingcoordinates corresponding to these locations; and using the coordinatesto extrapolate one or more points corresponding to one or moreadditional positions within the imaging region, respectively, therebycorrecting for spatial variations of the deposition-well plate.
 2. Thesystem of claim 1, wherein the capillary needle is or comprises steel,glass, or plastic.
 3. The system of claim 1, the system furthercomprising: a back-light illumination system co-located with themicromanipulator arm and capillary needle and oriented to project lightsuch that the microscope collects sufficient transmitted light to imageand analyze the individual cell of interest in this channel.
 4. Thesystem of claim 1, wherein the processor is configured to perform anautomated search for specific points on the deposition-well plate usinga software image analysis algorithm to detect the specific points. 5.The system of claim 1, wherein the processor is configured to performmulti-point calibration of imaging focus at one or more select locationsof the deposition-well plate using a software autofocus algorithmcomprising a focus scoring method and a one-dimensional root-findingalgorithm, and extrapolating the multi-point calibration for a pluralityof other locations of the deposition-well plate.
 6. The system of claim1, wherein the processor is configured to determine a spatial positionof the tip of the capillary needle based on one or more needlecalibration images.
 7. The system of claim 1, wherein the introductionof the volume of fluid comprising the individual cell of interest intothe capillary needle and the release of the individual cell of interestinto the first recovery well are conducted with or without a workingfluid, and/or with or without a micropump.
 8. The system of claim 7,wherein the introduction of the volume of fluid comprising theindividual cell of interest into the capillary needle and the release ofthe individual cell of interest into the first recovery well are furthercontrolled by the processor based on an image analysis algorithm andspatial data structure designed to trace locations of individual cellson the first recovery well and/or the capillary needle.
 9. The system ofclaim 1, wherein the individual cell of interest is a member selectedfrom the group consisting of a circulating tumor cell (CTC), alymphocyte, a leukocyte, a tumor cell, a stromal cell, a neuronal cell,a cell line, a stem cell, and an embryo.
 10. The system of claim 1, thesystem comprises a module (“Nanobox”) to automatically identifycandidate individual cells of interest, present images of candidatecells to a user, and automatically transfer chosen cells into recoverywells.
 11. The system of claim 10, wherein the system is furtherconfigured to detect and present dynamic behaviors of individual cellsof interest based on images taken at multiple time points; to trace thelocations of individual cells of interest over time; and to resolvepotential duplicates amongst candidate cells of interest due to anoverlap between adjacent images.
 12. The system of claim 10, wherein theprocessor is further configured with a module to define an optimal setof fluorescence intensity thresholds based on statistical and/or visualanalysis performed simultaneously with loading and processing of images;and/or to simultaneously present the images under screen cursor in allchannels and mark by a user locations of true positive individual cellsof interest that are either detected correctly or missed by theprocessor.
 13. The system of claim 1, wherein the cell of interest is aChinese Hamster Ovary (CHO) cell.
 14. The system of claim 10, whereinthe module identifies individual cells of interest based on cellmorphology.
 15. The system of claim 10, wherein the module presentsimages of candidate cells to a user assisted by a machine learningalgorithm.
 16. The system of claim 15, wherein the machine learningalgorithm is configured to suggest individual cells of interest.